United States
Environmental Protection
Agency
Environmental Research
Laboratory
Duluth MN 55804
EPA-600 3-80-048
May 1980
Research and Development
Air Pollution Studies
Near a Coal-Fired
Power Plant
Wisconsin Power
Plant Impact
Study
EP 600/3
80-048
LIBRARY
-'•loC-is H.J. 08SI7
-------
RESEARCH REPORTING SERIES
Research reports of the Oftice of Research and Development. U S Environmental
Protection Agency, have been grouped into nine series These nine broad cate-
gories were established to facilitate further development and application of en-
vironmental technology Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields
The nine series are
1 Environmental Health Effects Research
2 Environmental Protection Technology
3 Ecological Research
4 Environmental Monitoring
5 Socioeconomic Environmental Studies
6 Scientific and Technical Assessment Reports (STAR)
7 Interagency Energy-Environment Research and Development
8 'Special' Reports
9 Miscellaneous Reports
This report has been assigned to the ECOLOGICAL RESEARCH series This series;
describes research on the effects of pollution on humans, plant and animal spe-
cies, and materials Problems are assessed for their long- and short-term influ-
ences Investigations include formation, transport, and pathway studies to deter-
mine the fate of pollutants and their effects This work provides the technical basis
for setting standards to minimize undesirable changes in living organisms in the
aquatic, terrestrial, and atmospheric environments
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161
-------
EPA-600/3-80-048
May 1980
AIR POLLUTION STUDIES NEAR A COAL-FIRED POWER PLANT
Wisconsin Power Plant Impact Study
by
Kenneth W, Ragland
Bradley D, Goodell
Terry L, Coughlin
Department of Mechanical Engineering
University of Wisconsin-Madison
Madison, Wisconsin 53706
Grant No. 803971
Project Officer
Gary E» Glass
Environmental Research Laboratory-Duluth
Duluth, Minnesota
This study was conducted in cooperation with
Wisconsin Power and Light Company,
Madison Gas and Electric Company,
Wisconsin Public Service Corporation,
Wisconsin Public Service Commission,
and Wisconsin Department of Natural Resources
ENVIRONMENTAL RESEARCH LABORATORY-DULUTH
OFFICE OF RESEARCH AND DEVELOPMENT
U..S, ENVIRONMENTAL PROTECTION AGENCY
DULUTH, MINNESOTA 55804
-------
DISCLAIMER
This report has been reviewed by the Environmental Research
Laboratory-Duluth, U.S. Environmental Protection Agency, and approved for
publication. Approval does not signify that the contents necessarily reflect
the views and policies of the U.S. Environmental Protection Agency, nor does
mention of trade names or commercial products constitute endorsement or
recommendation for use.
i±
-------
FOREWORD
The U.S. Environmental Protection Agency (EPA) was created because of
increasing public and governmental concern about the dangers of pollution to
the health and welfare of the American people. Polluted air, water, and land
are tragic testimony to the deterioration of our natural environment. The
complexity of that environment and the interplay between its components
require a concentrated and integrated attack on the problem.
Research and development, the necessary first steps, involve definition of
the problem, measurement of its impact, and the search for solutions. The
EPA, in addition to its own laboratory and field studies, supports
environmental research projects at other insitutions. These projects are
designed to assess and predict the effects of pollutants on ecosystems.
One such project, which the EPA is supporting through its Environmental
Research Laboratory in Duluth, Minnesota, is the study "The Impacts of
Coal-Fired Power Plants on the Environment." This interdisciplinary study,
involving investigators and experiments from many academic departments at the
University of Wisconsin, is being carried out by the Environmental Monitoring
and Data Acquistion Group of the Institute for Environmental Studies at the
University of Wisconsin-Madison. Several utilities and state agencies are
cooperating in the study: Wisconsin Power and Light Company, Madison Gas and
Electric Company, Wisconsin Public Service Corporation, Wisconsin Public
Service Commission, and the Wisconsin Department of Natural Resources.
During the next year reports from this study, will be published as a
series within the EPA Ecological Research Series. These reports will include
topics related to chemical constituents, chemical transport mechanisms,
Diological effects, social and economic effects, and integration and
synthesis.
In this report, a product of the Air Pollution Modeling group of the
Columbia project, the authors apply a mathematical model, the Gaussian Plume
Model, to the specific conditions at the Columbia site. In order to assess
the model's accuracy, they then make detailed comparisons between the model's
predictions of sulfur-dioxide emissions and actual measurements of the
emissions from the stack.
Norbert A. Jaworski, Ph.D.
Director
Environmental Research Laboratory-Duluth
Duluth, Minnesota
111
-------
ABSTRACT
Concentrations and dry deposition of sulfur dioxide were investigated near
a new 540-MW coal-fired generating station located in a rural area 25 miles
north of Madison, Wis, Monitoring data for 2 yr before the start-up in July
1975 and for the year 1976 were used to assess the impact of the plume and to
investigate the hourly performance of the Gaussian plume model. The Gaussian
plume model was successful in predicting annual average concentrations (r =
0,95), but inadequate for simulating hourly averages (r = 0,36), The
incremental annual average increase in ambient 862 concentrations within 15 km
of the plant was 1-3 yg/m^.
Dry deposition of S02 was measured within the plume using the gradient
transfer method. An annual S02 dry deposition flux of 0,5 kg/hectare-year or
less within 10 km of the plant was inferred, which is about 3% of the regional
background deposition.
This report was prepared with the cooperation of faculty and graduate
students in the department of Mechanical Engineering at the University of
Wisconsin-Madison,
Most of the funding for the research reported here was provided by the
U,S, Environmental Protection Agency, Funds were also granted by the
University of Wisconsin-Madison, Wisconsin Power and Light Company, Madison
Gas and Electric Company, Wisconsin Public Service Corporation, and the
Wisconsin Public Service Commission, This report was submitted in fulfillment
of Grant No, R803971 by the Environmental Monitoring and Data Acquisition
Group, Institute for Environmental Studies, University of Wisconsin-Madison,
under the partial sponsorship of the U,S, Environmental Protection Agency,
The report covers the period of 1 July 1975 to 1 July 1978, and work was
completed as of January 1979,
iv
-------
CONTENTS
Foreword iii
Abstract iv
Figures vi
Tables x
Acknowledgment xii
1. Introduction 1
2. Conclusions and Recommendations 3
3. Validation Study of the Gaussian Plume Model 5
Mathematical development of the model 5
The data base 14
Tne computer program GAUSPLM 25
Results of the validation study 31
Average concentrations at the seven monitoring sites .... 55
Worst-case or highest sulfur dioxide concentrations 55
Mobile measurements of air pollutants downwind of the
stack 73
Predicted annual concentrations of sulfur dioxide, nitrogen
oxides, and particulate matter 73
4. Dry Deposition of Sulfur Dioxide from the Columbia Plume .... 78
Theory of gradient-transfer method 79
Experimental technique 81
Data collection and analysis 82
Results and discussion of deposition measurements 91
5. Calculation of Dry Deposition of Sulfur Dioxide from the
Columbia Plume 93
References 94
Appendix
Printout of program GAUSPLM 96
-------
FIGURES
dumber Page
1 Reflection of diffusing cloud by ground level and by
inversion lid 8
2 Effective stack height 13
3 Crosswind dispersion coefficients 15
4 Vertical dispersion coefficients 16
5 Location of SC>2 monitoring sites in the vicinity of the
Columbia Generating Station 19
6 Stack gas flow, stack temperature, and heat input in relation
to gross megawatt load at the Columbia Generating Station ... 22
7 Frequency distribution of stability class (A-E) occurrences
based on the Hino stability typing scheme 26
8 Frequency distribution of wind speed and wind direction at the
Messer site, 1 January-31 December 1976 27
9 Sector angle as a function of plume width 29
10 Frequency distributions of calculated and observed SC>2
concentrations for all occurrences 32
11 Scatter plot of hourly data points of calculated and observed
SC>2 concentrations for all occurrences 33
12 Frequency distributions of calculated and observed S02
concentrations for nighttime occurrences 35
13 Scatter plot of hourly data point of calculated and observed
SC>2 concentrations for nighttime occurrences 36
14 Frequency distributions of calculated and observed SC>2
concentrations for class A stability occurrences 37
15 Scatter plot of hourly data points of calculated and observed
S02 concentrations for class A stability occurrences 38
VI
-------
16 Frequency distributions of calculated and observed S02
concentrations for class AB stability occurrences ....... 39
17 Scatter plot of hourly data points of calculated and observed
S02 concentrations for class AB stability occurrences ..... 40
18 Frequency distributions of calculated and observed SC>2
concentrations for class B stability occurrences ........ 41
19 Scatter plot of hourly data points of calculated and observed
concentrations for class B stability occurrences ...... 42
20 Frequency distributions of calculated and observed SC>2
concentrations for class BC stability occurrences ....... 43
21 Scatter plot of hourly data points of calculated and observed
SC>2 concentrations for class BC stability occurrences ..... 44
22 Frequency distributions of calculated and observed S02
concentrations for class C stability occurrences ........ 45
23 Scatter plot of hourly data points of calculated and observed
SC>2 concentrations for class C stability occurrences ..... 46
24 Frequency distributions of calculated and observed SC>2
concentrations for class CD stability occurrences ....... 47
25 Scatter plot of hourly data points of calculated and observed
302 concentrations for class CD stability occurrences ..... 48
26 Frequency distributions of calculated and observed S02
concentrations for class D stability occurrences ....... 49
27 Scatter plot of hourly data points of calculated and observed
S02 concentrations for class D stability occurrences ..... 50
28 Frequency distributions of calculated and observed S02
concentrations for class E stability occurrences ....... 51
29 Scatter plot of hourly data points of calculated and observed
S02 concentrations for class E stability occurrences ..... 52
30 Frequency distributions of calculated and observed S02
concentrations for occurrences at the Portage cemetery
site (site 002) ........................ 56
31 Scatter plot of hourly data points of calculated and observed
S02 concentrations for occurrences at the Portage cemetery
site (site 002) ........................ 57
vii
-------
32 Frequency distributions of calculated and observed 302
concentrations for occurrences at the Lake George site
(site 003) 58
33 Scatter plot of hourly data points of calculated and observed
302 concentrations for occurrences at the Lake George site
(site 003) 59
34 Frequency distributions of calculated and observed S02
concentrations for occurrences at the Dekorra site
(site 004) 60
35 Scatter plot of hourly data points of calculated and observed
302 concentrations for occurrences at the Dekorra site
(site 004) 61
36 Frequency distributions of calculated and observed S02
concentrations for occurrences at the Messer site
(site 005) 62
37 Scatter plot of hourly data points of calculated and observed
SC>2 concentrations for occurrences at the Messer site
(site 005) 63
38 Frequency distributions of calculated and observed 302
concentrations for occurrences at the Genrich site
(site 008) 64
39 Scatter plot of hourly data points of calculated and observed
302 concentrations for occurrences at the Genrich site
(site 008) 65
40 Frequency distributions of calculated and observed S02
concentrations for occurrences at the Bernander site
(site 009) 66
41 Scatter plot of hourly data points of calculated and observed
S02 concentrations for occurrences at the Bernander site
(site 009) ' 67
42 Frequency distributions of calculated and observed 302
concentrations for occurrences at the Russell site
(site 010) 68
43 Scatter plot of hourly data points of calculated and observed
302 concentrations for occurrences at the Russell site
(site 010) 69
44 Annual averages of calculated and observed 302 concentrations
for all seven sites 71
viii
-------
45 Calculated 1976 average concentrations of 302 (yg/m3) near
the Columbia Generating Station 74
46 Calculated 1976 average concentrations of NOX (yg/m3) near
the Columbia Generating Station 75
47 Calculated 1976 average concentrations of particulate matter
(yg/m3) near the Columbia Generating Station 76
ix
-------
TABLES
Number Page
1 Definition of Pasquill Stability Classes 11
2 Pasquill Stability Classes as a Function of A T (AEG Typing
Scheme) 11
3 The Pasquill Stability Classes (A-F) Modified by the
Meteorological Agency of Japan (Hino Typing Scheme) 12
4 Fitted Constants for the Pasquill Diffusion Parameters 17
5 Information on S02 Monitoring Sites in the Vicinity of the
Columbia Generating Station 18
6 Distribution of Hourly Ambient 302 Concentrations at all Monitoring
Sites Before and After Operation of the Columbia Generating
Station 20
7 Maximum SOg Concentrations (yg/nP) for Various Averaging Times
Before (Pre-Op) and After (1976) Operation of the Columbia
Generating Station 21
8 Federal Ambient Air Standards for S02 23
9 Average Load, Coal Rate, and Emissions for The Columbia
Generating Station—1976 24
10 Meteorological Data Collected at the Messer Site 24
11 Sector Angle e(Ave.) as a Function of Stability 30
12 Analysis of Calculated and Observed S02 Concentrations According
to Classes of Atmospheric Stability 53
13 Analysis of Calculated and Observed S02 Concentrations at Each
Monitoring Site 70
14 Calculated and Observed Average S02 Concentrations (yg/m3)
at the Seven S02 Monitoring Sites Arranged in Order
of Decreasing Value 72
-------
15 Worst-Case or Highest SC>2 Concentrations (pg/m3) at the
Columbia Generating Station—1976 . . 72
16 Summary of 302 Mobile Monitoring Data Near the Columbia
Generating Station 77
17 Data from Eight Field Tests in which S02 Deposition was
Measured Near the Columbia Generating Station 83
18 Summary of SC>2 Deposition Measurements—Reduced Data 92
19 Summary of Total-Deposition-flesistanoe, Aerodynamic-Resistance
and Surface-Resistance Data for Sulfur Dioxide 92
20 Deposition of SC>2 from the Plume at the Monitoring Sites 93
xi
-------
ACKNOWLEDGMENT
Meteorological data were supplied by Prof» C»R. Stearns and B, Bowen»
Monitoring data and emissions were supplied by the Wisconsin Power and Light
Company. The cooperation of Keith Parker and Ben Ziesmer of the Wisconsin
Power and Light Company is greatly appreciated. The encouragement of Dan
Willard and the administrative help of Jim Jondrow in the project office in
the Institute for Environmental Studies bear special mention,
xii
-------
SECTION 1
INTRODUCTION
Gaseous and particulate air pollutants emitted into the ambient air by a
large coal-fired electric power generating station are transported and
diffused by the wind and are removed from ambient air by dry deposition,
precipitation scavenging, and chemical transformation. Environmental impact
is caused by excessive ambient air concentrations of various trace gases, fly
ash, and aerosol and by deposition of these components to the ground. Sulfur
dioxide and nitrogen oxides are the most voluminous of the gaseous pollutants
emitted; sulfate aerosol and fly-ash particulate matter are the most
significant liquid and solid pollutants emitted. This study focused on sulfur
dioxide because more sulfur dioxide is emitted from the stack than any other
pollutant and because extensive S02 monitoring equipment and monitoring data
were available.
The object of this study was to investigate the ambient air concentrations
and dry deposition near the Columbia Generating Station. The approach was to
validate a plume model by using sulfur dioxide monitoring data, and then use
this model to infer the concentrations of nitrogen oxides and fly ash. The
Gaussian plume model was used since it is in widespread use today, but has
never been completely validated. The dry deposition of S02 from the plume to
the ground near the generating station was also investigated with a series of
field measurements and computer calculations.
This work is part of a larger study entitled "The Impacts of Coal-Fired
Power Plants on the Environment," which is sponsored by the Environmental
Protection Agency-Duluth. The Columbia Generating Station is a new power
plant 25 miles north of Madison, Wis. Unit I (527 MW) came on line in the
summer of 1975. The station burns coal from Colstrip, Mont., which averages
0.8$ sulfur. An electrostatic precipitator is used. There is no other
flue-gas control equipment. The stack is 500 ft high. The utility is owned
primarily by the Wisconsin Power and Light Company, and their cooperation is
greatly appreciated.
Except for the city of Madison, which is 25 miles south of the station,
there are no other major sources of air pollution within a 75-mile radius of
the site. The town of Portage (population 7,800), M miles north of the stack,
has no major emission sources. The terrain in the vicinity of the site is
very flat except for the west-southwestern sector where the eastern edge of
the Baraboo Bluffs range approaches to within 4 miles of the stack. The
Baraboo range consists of wooded, rolling hills which rise to 600 ft above the
-1-
-------
base of the stack. The rest of the land is farmland with occasional woodlots
and extensive wetlands.
After summarizing the conclusions and recommendations, the validation
study of the Gaussian plume model is presented. Then the measurements of
sulfur dioxide dry deposition in the plume and the calculations of deposition
flux are presented.
-2-
-------
SECTION 2
CONCLUSIONS AND RECOMMENDATIONS
The overall results of this validation study, as shown in Figures 10 and
44, reveal that the Gaussian plume model is quite satisfactory for the
prediction of annual average concentrations of sulfur dioxide near a
coal-fired generating station. There were 492 h during 1976 when the
generating station plume registered more than 10yg/m3 of S02 above the
background levels at one of the seven monitoring sites. The correlation
coefficient based on the average calculated and average observed
concentrations at each of the seven sites when the plume was present was
0.954. Hence the Gaussian plume model can be expected to yield accurate
results for annual average calculations of nonreactive air pollutants.
Even though the model predicts well on the average, much more work should
be done to improve the model's ability to predict accurately the various
stability classes. The model tends to slightly underpredict for stability
classes A, AB, B, and BC; overpredict for classes C and CD; and underpredict
for classes D and E. Since the model is most sensitive to changes in the
dispersion parameters a v and az, more research is necessary to find values of
Oy and a z which pertain strictly to emissions from tall stacks.
The model tends to .underpredict during hours of light winds or near calms.
During these periods it is extremely difficult to model the plume because of
isolated wind puffs that affect the dispersing cloud in many different ways.
Another tendency of the Gaussian plume model is to slightly underpredict
for the monitoring sites farthest away from the stack and to overpredict for
the sites nearest the stack.
On an hour-by-hour basis for each of the 492 h when the plume was present
at a monitoring site, the correlation coefficient between measured
concentrations (with the background removed) and the model output was only
0.36. The highest observed hourly concentration during 1976 (with the
background removed) was 247yg/m3,' and the highest model output was 157ug/m3
for the sector-averaged value, which corresponds to a plume centerline
concentration of 245yg/m3. .The highest observed and calculated values did
not occur at the same time, however.
Improvements of the hour-by-hour correlation coefficient seem to hinge on
better knowledge of the input data. The sector-averaged concentration proved
more appropriate for the validation study. The plume centerline concentration
-3-
-------
best represents the worst-case 1-h concentration. The utility of the Gaussian
plume model to predict hour-by-hour concentrations over a year is doubtful.
Mobile monitoring data confirmed the general range of levels predicted by
the Gaussian plume model.
The model does an accurate job of predicting annual average concentrations
from 5 to 15 km from the stack. This conclusion is further borne out by the
fact that annual average concentration due to the generating station over the
5,929 h of the year for which the data were complete was 1-3u g/m3 within 15
km of the stack. This annual incremental increase is roughly the same as that
noted in Table 7 between 1976 and the pre-operation monitoring data. However,
the fine tuning of the Gaussian model to predict concentrations more
accurately in each stability category needs further work.
Dry deposition measurements of S02 at the plume-surface interface by using
the gradient-transfer method showed no evidence that a transient plume
resulted in higher deposition velocities than would be expected due to slowly
changing background concentrations. Sulfur dioxide deposition velocities were
0.3 cm/sec in pasture land, 0.75 cm/sec in marsh land, 1.8 cm/sec in a tall
prairie, 0.21 m/sec in a dry prairie, and 0.55 m/sec on snow. The tests were
difficult to conduct because of the highly transient nature of the plume;
continuation of this approach does not appear feasible.
Calculation of the SC>2 dry deposition from the plume, by using the ambient
air monitoring data and the deposition velocities showed that flux was 0.5
kg/hectare-year or less within 10 km of the generating station, which is only
3% of the regional background depositon flux.
The data base of hourly emissions, monitoring data, and meteorological
data provides an excellent opportunity to validate other atmospheric plume
models. We recommend that a grid model with deposition and chemical
transformation be run, compared to the monitoring data, and extended to a
larger region to investigate regional effects of the generating station.
-4-
-------
SECTION 3
VALIDATION STUDY OF THE GAUSSIAN PLUME MODEL
In this section the mathematical background of the Gaussian plume model is
developed; the data base for the validation study is presented; the computer
program GAUSPLM is described; and the results of the validation study are
presented in detail,
MATHEMATICAL DEVELOPMENT OF THE MODEL
Ambient air concentrations of sulfur dioxide and other pollutants that are
emitted by an elevated point source such as a power plant are often calculated
by using a so-called Gaussian plume model. The Gaussian plume model is widely
used for this type of application, and although there are numerous names for
the model they are all basically the same. In spite of the widespread
utilization of the model for environmental impact assessment, field validation
of the model is relatively sparse. This report presents the results of a
validation study of the Gaussian plume model and the associated empirical
constants.
Sulfur dioxide (S02) is the primary pollutant used in this validation
study for two reasons: (1) More S02 is emitted from the stack than any other
pollutant, and (2) ambient levels of S02 are recorded continuously at seven
monitoring sites throughout the study region. Hourly meteorological data and
hourly generating station data were used in conjunction with the hourly S02
data to validate the model, Theoretical average concentrations were
calculated for those hours when the plume was determined to be at a monitoring
site. During the study year 1976 an annual average calculated value and an
annual average observed value for each of the seven S02 recording stations
were determined. The hourly data that form the annual averages were examined
by means of frequency distributions and scatter plots. The results were
divided into stability classes to show model tendencies to overpredict and
underpredict for the various classes.
The variables in atmospheric diffusion are so complex that no completely
rigorous mathematical solution has yet been developed, but a statistical
representation of the problem is often satisfactory. Therefore, one widely
used approach is based on the idea that the concentration distribution of a
dispersing plume or cloud is Gaussian, To understand this representation a
review of the major points of the Gaussian diffusion theory developed by
Sutton (1953) is useful.
-5-
-------
The Gaussian Model
Since the diffusion of pollutants in the atmosphere is really a mass
transfer problem, mass transfer theory serves as a basis for the Gaussian
diffusion theory. From Pick's law of diffusion the rate of diffusion, Nx, of
a gaseous species in the x direction at some cross-sectional area, A, is given
by the expression
N = -K 3C/3x,
xx , ,.
where Nx is the mass transfer per unit time per unit area; Kx is the mass
diffusivity in the x direction; and C is the mass concentration per unit
volume. Pick's law of diffusion applies to laminar flow and is assumed to
hold for turbulent flow as well.
Gaussian theory applies this general equation to the diffusion of a gas
carried downwind (x direction) with wind speed, u, which originates from a
continuous source, through a differential volume in space. The horizontal and
vertical velocity components, v and w, are assumed zero. Therefore, from the
continuity equation u does not vary in the x direction, making the flow field
uniform. For flow in the x direction only, the species continuity equation
takes on the following form:
8C 3C , 3 , 3Cv , 3 (v 3C. 3 3C,
-r— = -u -5— + -5— (K -5—) + -5— (K -T—) + -T— (K -r—) . (2)
3t 3x 3x x 3 x 3y y 3 y 3z z 3 z v '
Equation (2) can be simplified to a more reasonable form with the following
assumptions:
(1) Mass transfer in the x direction is due mainly to the motion of the
wind; therefore, the diffusion term in the x direction, —(K -5—) may
o X X d X
be negligible;
(2) only steady state solutions are considered, hence 3C/3t = 0 ; and
(3) the mass diffusivities Ky and Kz are assumed constant.
After these three simplifying assumptions, Eq. (2) reduces to
u. 3C/3x = K 32C/3y2 + K 32C/3z2 . (3)
y z
If a further assumption is made (M), namely, that the wind speed u is
constant, then the general solution to this second-order partial differential
equation is:
C = Kx exp
2 2
_ ri_ + i_i _ji
IK K J 4x '
•-
(4)
where K is a constant whose value is dependent on the choice of boundary
conditions. One boundary condition that must be satisfied is that in any y-z
plane downwind from the point source the mass transfer rate must be constant
-6-
-------
and equal to the pollution emission rate Q; that is, all pollutant transport
downwind must be accounted for. For this to be true, two more assumptions
must be made:
(5) No chemical reactions occur in the plume; and
(6) the ground acts as a perfect reflector, that is, no deposition to the
ground is considered.
If a second boundary condition is satisfied, namely, that the point source is
located at some distance above ground, and an assumption (7), that the terrain
is uniform, is made, it can be shown that
K = Q/4ir(K K )1/2.
y z
2 x
After defining a = 2K -^ , where a = crosswind dispersion coefficient, and
j J j
2 x
a = 2K -jj , where a = vertical dispersion coefficient, Eq. (4), after
Z Z £t
substitution of Eq. (5), may be written in the following form:
2
1 / w i
C = -= exp
2irua a ^
y z
(6)
Equation (6) is a double Gaussian distribution in the two coordinate
directions y and z, but this solution to the general diffusion equation given
in Eq. (2) takes on the Gaussian distribution form only after the application
of the seven simplifying assumptions. The real distribution of atmospheric
pollutants at any instant may or may not be Gaussian; however, it may be
assumed so as a first approximation, so that equations such as Eq. (6) may be
used to represent the average concentration distribution over a short time
interval.
In the modeling of air pollution it is often necessary to take into
consideration the height to which pollutants may rise. At this height (often
referred to as the mixing height) a usually thin atmospheric layer exists in
which there is little fluid motion. This layer, which is formed by the
stabilizing effect of gravity, acts as a diffusion lid or ceiling; such an
inversion ceiling stops the upward dispersion of effluents.
In light of the previous discussion on the Gaussian distribution of air
pollutant concentrations, the mixing height, along with the ground, serves to
reflect the plume as shown in Figure 1.
-7-
-------
REFLECTED TRANSPORT LINE
Figure 1. Reflection of diffusing cloud by ground level and by inversion lid.
When either of the reflected lines reaches the other boundary level, it is
reflected, and so on. Mathematically, each reflection may be represented by
an image source. The result is an infinite series of exponential terms. For
a point source of emission strength Q at height h above the ground, and for a
mixing height of H, the ground level concentration field in a uniform wind u
is given by the equation
C =
2ira a
Y
exp
20
£°° / -(h-2nH)'
L exP( — —~
n=-°° V 20
N 7
(7)
Equation (7) (Casanady 1973) is the basic equation used in this validation
study of the Gaussian plume model.
Before Eq. (7) can be implemented for use by the computer, the infinite
series must be rewritten in a more usable form. This can be done as follows:
I exp
n = -°°
-(h-2hH
exp
20
^ \ /
) = n= - eXP (
^
r ) exP (
/ \
20
2nhH
2
2hnH
2 2
2n/H/
Ll
Since 2(j2 is independent of n, it is a constant and may be placed in front of
z
the summation:
I
-(h-2nH)
- 2
2o
exp
/-h
\2o
exp
2nhH
2 2
2n H
n—
-8-
-------
Now let us do the first few terms of the summation:
exp
-(h-2nH)
- 2 -
2a
exp
-h
2
z
1 + exp
2(l)hH
2(1)2H2'
+ exp
+ exp
2(-l)hH _ 2(-
2(-2)hH
2)hH
2(
2 ; •*• exp i 2
°z ' V az
-2)2H2 \
2 ) + "
a / J
2
a
z
(7f)
(7g)
After inspection, it can be seen that the exponential terms repeat in the
form:
exp
2nhH 2n2H2\ . /-2nhH 2h2H2
+ exp = 5-
as n ranges from 1 to <». Therefore the infinite series may be be rewritten:
exp -
(h-2nH)
2a2
= exp
-h"
2
z
1 + I
n=l
exp
2nhH-2h2H2
+ exp
2 2
-2nhH-2n H
Equation (7) then takes on the form
-= 2 exp —*-
2ir a a u I 0
y z \ 2a
exp
-h
2a'
+ exp
-2nhH-2n2H2 xn
1 + I
n=l
exp
2nhH-2h2H2
-x
Since the exponential terms in the summation are of the form e , the upper
bound of the summation may be replaced by some integer N such that for any
n _> N, the exponential terms are approximately zero. Because of the nature of
the constants, a value of 10 for N is sufficient to represent the problem.
-9-
-------
The Gaussian plume model has come into widespread use since its first
appearance in the late 1940's. Recently, it has become the most frequently
used air-quality simulation model (Sauter 1975). Studies involving the
Gaussian diffusion equation have been made by Klug (1975), Lee et al. (1975),
Mills and Record (1975), Mills and Stern (1975), and Bowers and Cramer (1976).
Both the U.S. Environmental Protection Agency and the Wisconsin Department of
Natural Resources use Gaussian-type diffusion models.
As is the case with any model, the Gaussian model has some limitations.
One limitation is the assumption that the wind field is constant and uniform.
In practice its use is therefore limited to time periods of several hours and
distances less than 30 km. Further, the Gaussian model is not applicable on
calm or nearly calm days. The omission of atmospheric chemical reactions and
pollutant deposition may become important as one moves farther away from the
point source. However, within 10-20 km of the stack the above factors are
expected to have little effect on the observed concentrations. Finally, the
model is not accurate in situations with complex topography.
The advantages of the Gaussian model far outweigh the disadvantages. Its
relative prediction accuracy is the most important factor. Klug (1975) has
shown a ratio of calculated to observed annual concentration of 1.25. Lee et
al. (1975) showed that the second highest hourly SOj concentration could be
calculated within a factor of 2 at two-thirds of the sampling sites, and the
ratio of predicted to measured second-highest 2M-h concentration ranged from
0.2 to 2.7 at 90$ of the sites.
Atmospheric Turbulence, Stability, and Turbulent Diffusion Typing Schemes
The dispersion of pollutants is accomplished by wind advection and
atmospheric turbulence. For most air pollution problems turbulence includes
wind-flow fluctuations with a frequency greater than 2 cycles/h. The most
important fluctuations are in the range of 1-0.01 cps. Atmospheric turbulence
is the result of atmospheric heating or cooling caused by a temperature
difference between the air and the ground, and mechanical turbulence produced
by wind-shear effects.
Since the dispersion of pollutants is dependent on the state of the
atmospheric turbulence, it is useful to describe the boundary-layer turbulence
in terms of the meteorological quantities that most affect it, namely, the
vertical temperature gradient and the horizontal wind speed. Theoretical
relations between these quantities and vertical diffusion are known, but the
lateral spread is not well understood. Therefore, turbulence typing schemes
that are empirically based have been developed to handle practical atmospheric
dispersion problems.
Probably the most widely used typing system is based on the scheme
proposed by Pasquill (197H) of the British Meteorological Office. Pasquill
created seven stability classes based on varying amounts of turbulence (Table
1). Since stability near the ground is primarily dependent on net radiation
and wind speed, Pasquill's typing scheme relates various combinations of these
two variables to his stability classifications.
-10-
-------
TABLE 1 . DEFINITION OF PASQUILL STABILITY CLASSES
Class Definition
A Extremely unstable
(extreme turbulence)
B Unstable
C Slightly unstable
D Neutral
E Slightly stable
F Stable
G Extremely stable
(no turbulence)
The U.S. Atomic Energy Commission (AEC) (1972) developed a turbulence
typing scheme that relates the vertical temperature gradient to the Pasquill
categories (Table 2). Another typing scheme, developed by Hino (1968) of the
Meteorological Agency of Japan, relates solar radiation and wind speed to the
Pasquill stability classes (Table 3).
TABLE 2. PASQUILL STABILITY CLASSES
AS A FUNCTION OF AT (AEC TYPING SCHEME)
Class Temperature change with height (°C/100m)
A
B
C
D
fi
F
G
<-1
-1
-1
-1
-0
1
>4
.9
.9
.7
.5
.5
.5
.0
to
to
to
to
to
-1
-1
-0
1 .
4.
.7
.5
.5
5
0
Of course the three typing systems listed above were designed to yield the
same result. The choice of which typing scheme to use depends on the
meteorological information available. In this validation study both the AEC
-11-
-------
TABLE 3. THE PA3QUILL STABILITY CLASSES (A-F) MODIFIED BY
THE METEOROLOGICAL AGENCY OF JAPAN (HINO TYPING SCHEME)
Night
Overcast
(10-8)*
Insolation High cloud (10-5)a
(cal/cm2/h)
Surface wind (Day and Middle or low cloud Cloud amount
speed (m/sec) >50 30-25 <25 night) (7-5)a (4-0)a
<2
2-3
3-4
4-6
>7
A
A-B
B
C
C
A-B
B
B-C
C-D
D
B
C
C
D
D
D
D
D
D
D
___
E
D
D
D
«.«-.
F
£
D
D
Represents cloud cover in tenths.
and the Hino method were tried. The Hino method turned out to be more
realistic. The reasons for this choice will be discussed later in light of
the meteorological data.
The consideration of turbulence, stability, turbulent diffusion typing
schemes, and their relation to atmospheric dispersion gives rise to one
question: What effects do turbulence and stability have on the Gaussian-type
equation developed above? These aspects of the problem will be explained in
detail in the following sections ori plume rise and dispersion coefficients.
Plume Rise
To simplify the treatment of dispersion, it is convenient to assume that
plume diffusion begins from a fictitious height above the actual source
instead of rising and diffusing as it actually does. This fictitious height
is called the "effective stack height" or the height of the point source
[variable h in Eq. (7)1. It is equal to the sum of the actual stack height
(hs) and the rise of the plume after emission (Ah) (Figure 2).
Plume rise (Ah) is a result of two separate effects: The momentum of the gas
leaving the stack and the buoyancy effect that occurs because the stack-gas
exit temperature is higher than the ambient air temperature. Plume rise
continues until the gas loses its momentum and until the gas sufficiently
mixes with the atmosphere to lose the effects of buoyancy.
The extent of the plume rise is closely related to the amount of
turbulence present in the atmosphere. A literature search yielded literally
scores of equations giving plume rise as a function of various stack
parameters, wind speed, and atmospheric turbulence. Briggs (1971, 1972) has
done extensive work on plume-rise calculation; he has found mathematical
relationships that show plume rise as a function of stack heat flux, wind
-12-
-------
THEORETICAL ORIGIN
OF DISPERSING PLUME
Figure 2. Effective stack height.
velocity, stack height, and stability. The following equations developed by
Briggs are used in this validation study: For unstable and neutral conditions
i / i ? / ^
i / j /, ȣ/j
Ah
2.47
<
u
for stable conditions
1/3
Ah = 2. 45
0.0064u
where QH = stack gas heat flux (kcal/sec), hs = stack height (m), and u = wind
speed at stack height (m/sec).
Actual measurements of plume rise (Bacci et al. 1974, Bowers and Cramer
1976) show that Briggs' equations give the best agreement. A study of a West
Virginia power plant shows a ratio of calculated plume rise to observed plume
rise of 1.08 (Bowers and Cramer 1976). Briggs' equations are in widespread
use (Klug 1975, Mills and Record 1975, Mills and Stern 1975, Bowers and Cramer
1976).
Dispersion Coefficients ay and 0Z
Application of the Gaussian diffusion equation [Eq. (7)] requires
knowledge of the vertical and horizontal growth of the plume. This growth is
usually expressed in terms of the standard deviation of the concentrations in
the crosswind and vertical directions, a and a respectively. It is
primarily in terms of these two parameters thatzthe use of the Gaussian form
in Eq. (7) maintains flexibility, because different methods of obtaining o
and az can be used without changing the whole computation system. y
-13-
-------
Many empirical functions have been proposed by investigators to represent
the dispersion parameters a and az as functions of downwind distance (x) and
atmospheric stability. The most widely used functions for the dispersion
coefficients are based on the work of Pasquill in a form presented by Gifford
(1976). A convenient graphic presentation is given by Turner (1970), who
indicates that these values are representative for a sampling time of minutes
to hours and apply strictly to low-level releases over open, level terrain
(Figure 3( Figure 4). The graphs of the Pasquill diffusion parameters have
been approximated in this study by power law relationships of the
form a = bxq , where b and q are given in Table 4 (Gifford 1976).
Studies of power plants (Barber and Martin 1973, Bacci et al. 1974) have
shown that the empirical functions developed by Pasquill consistently
underpredict the actual values of plume height and width as measured by laser
techniques. The main reason for this discrepancy is that the Pasquill
diffusion parameters apply to low-level releases and not to the high-level
(large stacks) releases from power plants. Since no consistent set of data
for high-level releases has been developed, however, the Pasquill dispersion
coefficients are widely used.
THfi DATA BASE
The previous section was devoted to a discussion of the Gaussian plume
model and the parameters associated with it. These parameters were shown to
be fundamental to the accurate prediction of air pollution concentrations
downwind from a point source. Because these variables are dependent on the
condition of the atmosphere at any given time, sufficient meteorological data
are required to determine values for them. Also, ambient air data
characterizing the background concentration levels in the adjoining region and
emissions data from the source are essential. All three types of data were
obtained on an hourly basis for this validation study. In this section the
ambient SC>2 data, the station generating data, and the meteorological data
will be discussed in detail.
Ambient Sulfur Dioxide Data
The SC>2 monitoring network surrounding the Columbia Generating Station is
quite complete. Monitoring installations at seven sites (Table 5, Figure 5)
were in operation during the study year 1976. Four of the SC>2 monitoring
stations were in operation 2 yr before the generating station opened on 15
July 1975. For this reason the S02 background in the area near Portage, Wis.,
is well known.
Phillips Model PW9700 continuous S02 analyzers housed in
temperature-controlled buildings were used at all sites. The intake ports
were 1.8 m above the ground. The limit of detection of these instruments was
10 yg/m3. The instruments were fully calibrated once every 3 months and were
internally calibrated every 24 h. Data reduction to hourly average values was
-14-
-------
10,000
1,000- -
c.
<5
•*-
0)
>» 100- -
0.1
1 10
DISTANCE DOWNWIND, km
100
Figure 3. Crosswind dispersion coefficients,
-15-
-------
1,000- -
(A
0>
**
0)
E
N
1.0
0.1
1 10
DISTANCE DOWNWIND, Km
100
Figure 4. Vertical dispersion coefficients.
-16-
-------
3
w
H
JVj
§
^
M
O
4-1
03
4J
9
•M
03
e
0
CJ
X
v/
csl
X
a1
O
CM x-v
X! ^
c\
X!
v/
X
y
i—
X
&
o
xT '?
v~'
r-H
X
v/
X
cr
n
*o cd
•H g
^ tfl
CO 4-1 CO
CO CO
o a
(-( O
U 0
>,
4-J
•H CO
rH 03
•H CO
CO O
4J
CO
iH 0
i — i o CT> in
• • • •
CM r-t O O
VO
CM
CM CTN
o r^ I-H oo
O in i — !
o o I-H ro m
• • • • •
o o o o o
o o o o o
m o o o o
CM O O O O
iH rH iH rH
vo 1-1 r>« oo
CO 00 i — 1 CM ^
co cy* o*\ oo i^«
• • • • •
rH O O O O
in H
•H
CU
X M
tfl
CU
01
t£2 T3
& a
cfl
C^) N
o e>
• • M
o cr
Cfl fO
n n
>, N
G G
tfl XI
-17-
-------
TABLE 5. INFORMATION ON S02 MONITORING SITES IN THE VICINITY
OF THE COLUMBIA GENERATING STATION3
Site
number
002
003
004
005
008
009
010
Site
name
Portage
Cemetery
Lake George
Dekorra
Messer
Genrich
Bernander
Russell
Distance from -
station (km)
9.9
5.9
4.4
7.3
8.3
14.6
15.5
Direction from station
Direction
NNW
ENE
SW
W
N
E
NE
In degrees
332
69
227
267
7
97
37
Start of S02
monitoring
March 1973
March 1973
July 1973
March 1973
May 1976
May 1976
May 1976
Generating station began operation on 15 July 1975.
done manually and keypunched. A zero drift up to 25 ug/m3 could occur, but
this factor was corrected for in the data-reduction procedure. ^
A frequency distribution of hourly S02 concentrations averaged over all
the sites for 2 yr before operation of the generating station and for the
operating year 1976 is given in Table 6. The maximum S02 concentrations on a
1-h, 3-h, daily, monthly, and annual basis were generally lower for the
pre-operation period than for 1976 (Table 7). The monthly and annual average
concentrations are somewhat low because hourly concentrations less than
10 yg/m^, which could not be measured, were set equal to zero. In general the
S02 concentrations increased as a result of the operation of the generating
station. The data are examined further to separate the background levels from
the contribution of the station later in the report. The observed
concentrations are far below the federal ambient air standards (Table 8).
S02 data collection was the responsibility of the Wisconsin Power and
Light Company. Data reduction was done by the University of Wisconsin before
March 1976 and by the Dames and Moore Company after March 1976.
-18-
-------
rH
O
0)
rd
4-1
•rl
d
•H
O
•H
CO
CO
0)
4J
•H
cn
fi
•H
t-i
O
4-1
•H
C
Cs|4J
O fl
CO 4J
CO
ii |
O 00
d
d -i-i
O 4J
•H JO
4-1 ^
eg oi
o d
s
d
bO
•H
-19-
-------
TABLE 6. DISTRIBUTION OF HOURLY AMBIENT S02 CONCENTRATIONS AT ALL MONITORING
SITES BEFORE AND AFTER OPERATION OF THE COLUMBIA GENERATING STATION
Percentage time exceeded
Concentration
greater than: Before Columbia After Columbia
(yg/m3) operation (1973-75) operation (1976)
'10
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
12.9
6.75
2.03
0.701
0.340
0.166
0.0908
0.0545
0.0333
0.0227
0.0136
0.0121
0.0076
0.0045
0.0015
0.0000
15.0
9.17
3.75
1.72
0.856
0.501
0.306
0. 176
0. 113
0.0692
0.0422
0.0270
0.0220
0.0135
0.0084
0.0068
Station Generating Data
The plume rise and emissions data needed for input to the plume model were
obtained from stack tests and hourly records of gross megawatt load provided
by Wisconsin Power and Light Company. The plume rise depends on the heat flux
up the stack, Q^. In general, QJJ is a function of the stack gas volume flow
rate and the stack gas temperature, which are functions of the gross
generation load in megawatts. The functions that relate gas flow rate, gas
temperature, and heat input to the gross megawatt load will differ with each
boiler design. Tests were run in 1976 by the WPL to determine these relations
for the Columbia Generating Station. The test results are shown in Figure 6
and are given numerically:
CMS = 1.7GMW(IH) + 103.9,
TSK = 0.064GMW(IH) + 370.8,
QH = 84.88CMS(TSK-TAK)/TSK,
where CMS = stack gas flow rate (m^/sec), GMW(IH) = gross megawatt load at
hour IH, TSK = stack gas temperature (°K), TAK = ambient temperature (°K)
-20-
-------
13
O
CO M
rvj EH
§ <^
M H
H CO
o o
!3 S3
I — 1 i — I
0 H
PM PM
W W
> S3
> i-j
o
Pi U
o
PM M
K
co H
e
-NX^ pT-|
txO O
— j_
x_x £?••
5
CO H
§s
H W
0
ti
•H
OJD
cd
f_|
0)
<^
H
cd
0)
t~!
4J
c
O
g
r]
»^j-
CM
43
CO
i~j
, — |
QJ
4-1
•rl
CO
r- 4
•* ^— S
co ^o
r*-> r--
— x, -Xx,
CN CO
rH ^
^ ^f
00 rH
/"N y—S
-3" **O
r-- i*-*
-• — . -NX^
(^ rH
' — ^ rH
rH \
rH rH
~^-S \^S
m vo
^O LO
^-^
*»o
/^*v r*^«
LO -^.
r~-- i — I
-^ rH
CM ^\
•^ CO
LO
^
§ S
rH
1 0
rH rH
*• — •* ^~^
O CM
CM r~-
CM rH
s~^ s^*
CO \O
r^* r^
•^^ ^^
^t~ i — i
rH rH
•^. ^^^
ON CO
A *
s e
cd cd
O rH
rH rH
\ / \^/
\o o
CTi i — 1
CM CM
ft
O
1 *£)
0) r^
M ON
CM PM rH
o
0
rH -a-
• .
CO ^O
£
rH
^^
f VO
co r^
r^ ~~ —
\ CO
O> N— '
CM
ON CM
/-^
^*\ ^o
co r-~
r^- "* — .
"^^ VD
r-- —
~~~ CO
co -^
^~S
UO
CN] CO
^O rH
/*** N
s~^ **O
co r^
r-* ---^
-x^ Q^
r-*- rH
--^. — X.
oo co
* *
co cd
rH rH
1 1
0 O
rH rH
•^— ' **-s
CO rH
vD O
rH CNl
s~+^
y^~N \O
co r^
r^» '^'^w
— ^- O^
VD rH
rH ^-,
"*--. LO
r^*.
A
** s
S cti
cfl
rH
00 rH
V— •* ^^
r*1** -
CO PL, H
O
O
r^- LO
• •
•%»
-*x. ' — x.
CN LO
^ ^
rH VD
•^x, r~*^
uO ~^-
^-^. ^D
t*»N. r*^-
-x^ -^x
oo r^
*--x. -v-^.
o> LO
8 B
CX P,
•*O
>^o r*^
rH "^
— -x r^«
•^d" "*
LO
*
B •
ft B
rH
rH • — -*
^ O
Sf vO
S~*\ S~^
<$~ "O
r^ r^»
-NX. "- —
Q*\ ^
-«*x, CN|
rH \
rH LO
B" B"
ft cd
i — 1 CT»
1 1
O *»O
rH ^^
v — '
CO
UO LO
Q\ rH
s*\
Vj-
r^- s**\
\ \o
co r*--
Cs) \
O CN
tH -s^
LO
*
a •>
cd B
cd
rH
i — I r^
"*~s "• — '
-3" LO
SJ- CO
CM CO
ft
O
CD r^
JM ON
LO PM rH
O
0
/•— \ s~^
vD vO
r — > r^-
"NX. ^^.
r**> uo
^ ^
CO O
rH rH
/""N /*™N
vD vD
r-*- r^.
-•NX -NX
&\ CO
rH rH
—NX **
r-. uo
^-S ^x-^
O CO
r- co
/^N,
*^D /*~N
r-* ^0
*• — r^.
CT\ "-N.
rH 00
-NX, *NX,
r-* o>
r> *\
B e
& cd
0 O
rH rH
1 1
!*-« f-N
"* — •* ^ — •*
Nj- I^N
CN CN
CN rH
s-^
\D ^
r-> *^o
•N, [NX,
CO -^
rH OO
*-"x "-x.
r-N CTA
M <\
P €
CQ Co
O> CT»
N-rf' 'N^'
CO CO
UO CO
CO rH
OH O i
o o
1 ^O 1 ^^O
cb r~- cb r~
H ON SM ON
OO PL, rH ON P-i rH
O 0
0 O
y— \
^O
r-*
• — x
LO
S— '
CO
rH
y—S
^O
r->
*• —
^o
rH
"- —
vD
'N^'
vD
uO
/— ^
**o
f-N,
-NX,
CN
CN
— ^.
I-N,
*\
B
ft
CO
1
CM
rH
1 — '
00
CM
rH
/^-N
^O
r^
• — ^
CM
CM
~- —
r^.
*,
B
ft
rH
> — s
CM
<]•
rH
ft
O
a) r-»
M ON
O PM rH
rH
o
-21-
-------
2200n
o
„< 1800
o
^
7
o
«» 1400-
CO
O
o
(0
1000
Stack Gas Flow
Stack Temperature
100 200 300 400 500
Gross Generation In Megawatts
r290
r270
5
0)
CL
250 §
t-
o
(0
-230
600
5000
4200-
D
00
3400-
Q.
fj 2600-
CB
0)
X
1800
Heat Input
100 200 300 400 500
Gross Generation In Megawatts
600
Figure 6.
Stack gas flow, stack temperature, and heat imput in relation to
gross megawatt load at the Columbia Generating Station.
-22-
-------
TABLE 8. FEDERAL AMBIENT AIR STANDARDS FOR S02
Time of average (h)
3
24
Annual
Primary standard3
365 yg/m3 (0. 14 ppm) b
80 yg/m3 (0.03 ppm)
Secondary standard3
1,300 yg/m3 (0.5 ppm) b
260 yg/m3 (0. 10 ppm) b
60 yg/m3 (0.02 ppm)
aCorrected to 25°C and 760 mm Hg.
^Concentration not to be exceeded more than once per year; ppm on volume basis.
(from hourly meteorological data), and QH = heat flux from the stack
(Kcal/sec).
The average S02 emission rate measured in the stack tests was 1.74 lb/10°
BTU. Knowing that the average higher heating value of the coal is 8,662
BTU/lb, the emissions were 30.14 Ib SC>2/ton coal, and thus the emission rate
used in the modeling was
OJ302 = 3.8TCH (g/sec), (8)
where TCH = coal flow in tons/h.
The TCH was determined as a function of GMW:
If GMW > 400, then TCH = 0.54263MW + 1.15;
if 175 < GMW < 400, then TCH = 0.5137GMW + 12.4;
if GMW < 175, then TCH = 0.3394GMW + 42.74.
Knowing TCH as a function of GMW, the emission rate of SC>2 in grams per second
(QS02) can be calculated from Eq. (8).
In a similar fashion the emission rates of nitrogen oxides (NOX) and
particulate matter (PM) can be determined. Based on stack test results the
equations are as follows.
QNOX = 1.03TCH (g/sec) (9)
and QPM = (0.46. to 2.94 )TCH (g/sec). (10)
Operating problems have been experienced with the electrostatic precipitator
at the generating station. Consequently, the emission rate of particulate
matter can vary greatly at this time, as shown in the preceding equation.
The average load, coal rate, and emissions were calculated for the
Columbia Generating Station by using the preceding equations for the study
year 1976 (Table 9). For PM a coefficient of 1.31 was used. This corresponds
-23-
-------
to Unit I of the generating station meeting the federal standard for PM, which
is 0.61 lb/106 BTU. The averages are summarized in Table 9.
TABLE 9. AVERAGE LOAD, COAL RATE, AND EMISSIONS FOR
THE COLUMBIA GENERATING STATION--1976
GMW load
407.6
Coal input
( tons/h)
223.5
Emmission
S02
73-4
rate (metric
NOX
19.9
tons/ day)
PM
25.3
Meteorological Data
Windspeed, solar radiation, and temperature were measured at four sites.
The stations were located at the first four S02 monitoring sites (Table 5,
Figure 5). The meteorological data used in this validation study come from
the Messer site (Table 10).
TABLE 10. METEOROLOGICAL DATA COLLECTED AT
THE MESSER SITE
Item Units
Solar radiation cal/cm^/min.
Air temperature °C
32-m wind direction degrees
32-m mean wind speed in/sec
2-m mean wind speed m/sec
The solar radiation and the 2-m wind speed are used in conjunction with
the Hino method for determining stability. The Hino stability typing scheme
described in Table 3 uses wind speed at 10 m. However, a review of the actual
data showed that the 2-m wind speed at Messer was equivalent to the 10-m wind
speed measured at the other sites because of the higher elevation. The solar
radiation and wind speed should be measured at the same location to insure
proper use of the typing system.
The difference in air temperature at the Messer site between the 2-m and
32-m level was at one time used to determine the stability (AEC turbulent
typing scheme, Table 2). When these data were used, however, discrepancies
were noted. The temperature gradient gave some stability of class A during
January. Physically, this result is not expected since turbulence due to
atmospheric heating is much smaller in January than in July when the sun is
-24-
-------
near its summer solstice. The temperatures did not shift to a neutral case
during high winds as would be expected from turbulent diffusion theory.
Because of the discrepancies the method was discarded, and the Hino method was
used for the entire study year. A frequency distribution of stability class
occurrences based on this stability typing scheme is given in Figure 7.
One of the simplifying assumptions made in developing the Gaussian
equation was that the wind speed is uniform between ground level and mixing
layer; in reality this is untrue. However, in holding to the assumption, the
question arises as to which wind speed to use, one at 10 m, stack height,
mixing height, or somewhere in between. The wind speed most widely used
according to reports in the literature is that measured at stack height. The
32-m tower at the Messer site was designed to be at the same height above sea
level as the top of the 500-ft stack of the generating station. Therefore,
the Messer 32-m wind speed is a fair representation of the wind speed at stack
height, and this velocity is used in the program.
Wind direction is measured twice at each of the four monitoring sites
giving a total of eight possible wind directions. The Messer wind directions
are measured at 32 m, whereas the otner six are measured at 9 m. In agreement
with wind-shear theory the directions measured at 32 m are significantly
different from those measured at lower levels. The question again arises:
Which one should be used? Weidner (1976) showed that the wind directions
measured at Wyocena best represent the flow in the region near the generating
station. Unfortunately, for the study year 1976 the Wyocena wind-direction
data were not complete. In the computer model wind direction is not
considered exact, but is used as a reference position only. Therefore, since
the Messer wind direction is a good representation of the flow at stack
height, and because the Messer wind-direction data set is quite complete
(Figure 8), the wind direction is used in the validation study.
THE COMPUTER PROGRAM GAUSPLM
In previous sections we have developed the Gaussian diffusion equation,
discussed the dispersion parameters, and examined the data base. These three
quantities are united by the computer program GAUSPLM, from which the
validation study proceeds. How the program functions and how its output is
validated will be discussed in this section.
The Model
The program GAUSPLM is based on the Gaussian diffusion equation for the
dispersion of effluents from an elevated point source and uses the data base
discussed in the previous section. A printout of GAUSPLM is given in the
appendix. This model is slightly different from most models in that the
calculations of average S02 concentrations are based on an angle of plume
spread that varies with stability. These concentrations are determined only
when a monitoring site is in the plume. If a site is in the plume, then an
S02 background level is predicted, subtracted from the monitoring-site
reading, and the theoretical average is compared to this calculated observed
value with the background removed. In this manner the Gaussian diffusion
equation may be validated.
-25-
-------
T
O
in
i
o
o
o
CM
T
O
o
•H
w
fs
C
o
01
CO
CO
cu
u
c
cu
•>-<
V-i
3
O
O
o
w
4s
en
CO
rt
•H
,0
n)
O
•H
4J
,0
•rl
o
CO
60
u| Aouenbajj
O 4-1
C -H
CU ^1
cu cs
t-i -u
PM w
0)
S-i
3
toO
•H
-26-
-------
NW
WNW
10.6
W! (5.5)
wsw
N
NNW
NNE
SW
SSW
NE
ENE
ESE
SE
SSE
KEY:
3.7 Frequency (percent)
(3.7) Wind speed (m/s)
Figure 8. Frequency distribution of wind speed and wind direction at
the Messer site, 1 January - 31 December 1976.
-27-
-------
Method for Determining Sulfur Dioxide Background Level
Background levels of S02 are often of the same order of magnitude as the
SC>2 from the stack. This condition makes the removal of the background very
difficult indeed. However, a scheme has been developed to remove the
background S02, and it has been reasonably successful.
All of the SC>2 monito ring-site data are read into GAUSPLM in 1-month
periods. For each hour of the day the SC>2 readings from the sites are
compared to the average Messer wind direction. If a site is within 22.5° of
the plume centerline (plume centerline is the wind direction plus 180°) and
has a reading of at least 10 yg/m , it is assumed in the plume. The value
10 yg/m3 is used as this is. the detection limit of the Phillips SC>2 Monitor PW
9700. The S02 background is then considered to be the arithmetic average of
the readings from the other monitoring stations. If the SC>2 level for the
plume site is at least 10 yg/m3 greater than the calculated background, the
model calculates a theoretical average SC>2 concentration for that site. If
the difference is less than 10 yg/m3, the background S02 becomes the average
of all of the site readings, and no model calculation is made. For the cases
in which the plume is not near a monitoring station, the SC>2 background is
again the average of all site readings, and no further calculations are made.
Averaging Procedure
The Gaussian plume equation is very sensitive to wind direction. The wind
direction at Messer, even though representative of the flow at stack height,
does not necessarily represent the flow in the valley. If the direction is
off by as little as 10°, completely different concentrations will be
predicted. To avoid this problem, an averaging procedure was developed.
If a monitoring site is assumed to be in the plume, then the wind
direction is "swung around" so that the plume centerline goes through the
receptor point. An average concentration is then calculated over a sector,
whose angle changes with stability. Mathematically, this averaging procedure
is expressed thus:
2
9xC -
2ira a u
v z
dy I exp I -
(h-2nH)
2a2 / ' (11)
z
In effect, all of the concentration under the Gaussian distribution in the
crosswind direction is being summed up and put into an arc length of size 9x.
At this downwind distance x, over an angle of 6, the S02 concentration is
made constant.
In carrying out the integration, we note that
2 2
f e a X dx =
-28-
-------
If we apply this to Eq. (11),
exc = k__^..— »
I exp [ -
(h-2nH)
2a2
(13)
The concentration at the plume centerline (y=0) is given by
2
C(x,o)
2ira a u ^
y Z -oo
exp I -
(h-2nH)'
2a2
z
(HO
Equation (11) may now be solved for the average concentration, C:
c"
/ 2ir a C(x,o)
(15)
The average concentration C is now compared to the observed S02 reading at
the site in question. The averaging procedure was done in an attempt to
desensitize the Gaussian plume equation to wind direction. The sector
angle 0 is a function of the plume width (W) and the downwind distance x
(Figure 9).
Plume
Centerline
Figure 9. Sector angle as a function of plume width.
-29-
-------
The plume width is defined as that distance in the crosswind direction at
which the concentration falls to one-tenth of the centerline value. Relating
the plume width to o , it can be shown that
Y W = 4. 3a .
y
If we use the empirical expression for a given in Table 4,
0.903
ay = ax
where a is a constant varying with stability classification. The sector angle
(9) may be written as
Q 1 *. 1 / a i r ~0• 0 9 7 .
9=2tan (2.15 ax ).
From the above equation it can be seen that (6) is a function of stability
(a) and downwind distance x. Since 6 varies with x for a given stability,
a (Ave.) was determined by numerical integration. Values for x are the
downwind positions of the seven monitoring sites. Table 11 gives 6 (Ave.) as
a function of stability.
TABLE 11. SECTOR ANGLE 9 (AVE.) AS A FUNCTION OF STABILITY
Stability
class
A
A-B
B
B-C
C
C-D
D
E
Constant
a
0.4
0.3475
0.295
0.2475
0.2
0.165
0. 13
0.098
6 (Ave.) (degrees)
39.3
34.4
29.5
24.9
20.2
16.7
13.2
10.0
Model Output and its Use
The computer model GAUSPLM is run for the data year 1976. From this
year's worth of hourly data, the validation study was made. The output from
the program GAUSPLM contains the following information:
(1 ) Year
(2) Month
(3) Day
(4) Hour
(5) Monitoring site number
(6) Stability class
(7) Average Messer wind direction
(8) Average wind speed at stack height
-30-
-------
(9) Average background S02 value
(10)' Maximum 862 reading from the sites not in the plume (gives an idea of
background variance)
(11) SC>2 reading of the site in the plume
(12) Calculated theoretical centerline concentration
(13) Calculated average S02 concentration
(14) Value of observed SC>2 readings
The most important pieces of information are the predicted average and the
"calculated" observed concentrations of SC>2. These two numbers are used in
the validation procedure of the Gaussian plume model.
The concentrations are compared on an hour-by-hour basis in scatter plots
for the following conditions: (1) The year's data lumped together; (2) the
year's data broken down into stability classifications; and (3) the values at
each monitoring site. The data are then compared by using frequency
distributions. Scatter plots show the correlation of the predicted to
observed concentrations on an hour-by-hour basis. The frequency plots compare
the values on an overall distribution level. An annual average is predicted
and compared to the annual observed value for all sites. Annual averages are
also predicted for each individual site and compared to the observed averages
at the sites.
RESULTS OF THE VALIDATION STUDY
The model GAUSPLM was run for the study year 1976. For the 5,929
operational hours (i.e., those hours in which all data were available) the
plume was present at the sites for 492 h, or 8.3% of the time, based on the
criteria that the site in the plume was 10yg/m3 above the background. The
average calculated value of SC>2 when the plume was present based on all 492
occurrences was 43.5yg/m3. The average observed value during these hours was
44.2yg/m3. The ratio of the calculated average to the observed average is
0.9846, which is extremely close to 1. Given this information alone, we might
conclude that the Gaussian diffusion equation is very accurate in predicting
an annual average.
When the frequency distributions of the calculated S02 concentrations and
the observed SC>2 concentrations are compared, the results are indeed
reasonable (Figure 10). Both curves are of the same general shape, and their
peaks are within a factor of 1.43 of one another. The median value for the
observed concentrations is 28yg/m3, that for the calculated concentrations is
40 yg/m3. In general, the Gaussian model tends to underpredict for
concentrations less than 30yg/m3 and greater than 80 y g/m3 and overpredicts
for concentrations between these values. On the average, the model predicts
quite well.
The hourly data from which the frequency distributions were derived do not
correlate well. The 492 data points are shown collectively in the scatter
plot given in Figure 11. A correlation coefficient of 0.36 was calculated
based on these points. The 45-deg line going through the origin represents
the ideal case where the model correlates exactly with the observed. The two
other lines represent the initial error bounds which put the calculated values
-31-
-------
•
rt
*
S
O)
T3 T3 O
0) 0) '-
t; > «
— 0) EC
I 5
(o O
U
o>
0)
01
(0
w
0>
>
<
O
tf)
O
co
o
CN
O
O
CN
O
00
o
-co ^
DC
2 •-
O Z
^ LLJ
O
Z
o o
CO O
8 8
o
CM
CO
0)
o
01
o
u
o
CO
c
o
0)
o
a
o
u
r-J
O
CO
(-1
CD
CO
•a
03
cu
4J
cd
B)
O
cn
C
O
•H
4-1
,0
•H
o
c
a)
cr4
cu
1-4
INBOUBd Nl AON3nO3dd
bO
•H
-32-
-------
o
\ •
, \ • . • • •
\
\
\ °
x •
\ •
\ ,£
\ 7.
. \ i •
• \
. o
\ X
\
\
\
\
\ . •
\
o • \
X \
II .\
' .. \
\ . \
• \ \
\ V
\ . N
\ \
\ *\ * »
X • \
\ * * N . •
\ • • • \
\ • ' X .
\ ' . \ . • *
\ \
\ * •* \
• v * * * *
X • f _ y
• \
\ » X
V « • \
X- \ ... • \
CM X. * • • \
II v . - \
0 • . \ \
x • . \" • : \ . *
«x . . \ . »\ •
! • •• v . - • \ •• :
^ . • • \ , T
^. . • X" *. . \«
v . . \ * v .
^, .'.••• \.S '\
•^, * "' '/ V, \*
: '•' ' "*< • "» .. N . * >/• '
... -^ • • . x •• \ .
• . • " ^ *» N .A
. . .!• ^ • • • v . • .
. *•• •• 1 ^* »*v. \ •'
. «^x» *••••• ••
* . - *fc . _«. w •• —- I* . 1
. »^ • • ^ «•• ^\
• •»..«•• 2*^> • ^ . \ •
'- • . •' ..••>•. ' "^v •••V>^i\\.'.v
— »• .* *«.^ • AU^A » • * M
•• • • • »Wx • 1 •** *\ * \ <»
• . • .••-.• . • . . >«? '•*.• V«* t"*Z
. • • •«!••••. r«--. ••*t ^. •*
»• s »
^^\
xi
rO
CM
O
CO
^^^
0
(O
T"
h^
T-
o
CM
O
O
O
CO
.0
(O
o
^-
-8
666666
CM O CO CO ^ CM
^^ ^^
_t
(-LU/67/) suo^ejjuaouoo paje|no|eo =°x
CO
E
^
O)
~w
•^.
**^
(/)
C
g
(D
l^m
*-»
C
m
w
O
C
o
O
•o
0)
>
^^
4-l
CO
C
o
•H
4-1
CO
)-l
4J
a
)-l
0)
CO
43
O
•a
CO
"8
4-1
Cfl
H
3
O
H
CO
CJ
<4-4
O
CO
4-1
(3
•H
O
CLi
CO
4-1
Cfl
T)
>»
T-H
a
5
M-t
O
4-1
O
rH
CX
M
0)
4J
4J
Cfl
CJ
C/3
^H
rH
-------
within a factor of 2 of the observed values. Forty-eight percent of the data
points fall within a factor of 2 of one another.
Diurnal and Stability-Class Variations
Although the scatter plot in Figure 11 appears to be somewhat randomly
distributed, there is more orderliness than first meets the eye. A look at
the diurnal variation and the points broken down into stability classes
reveals model tendencies that are not apparent at first glance.
Figures 12 and 13 are the frequency and scatter plots of the nighttime
occurrences. Ninety-four of the 492 points (1950 occurred between the hours
of 5 pm and 7 am. Of these 94 points only 17 (18£) fell within a factor of 2
of one another. Both of the plots reveal the model's tendency to underpredict
the observed concentrations. The average calculated value of 4.7 Mg/m3 is
roughly one-fifth as large as the average observed value of 21.9 yg/m3.
Reasons for this underprediction will be discussed later.
The most useful information is obtained when the data points are divided
into stability classes; the model prediction tendencies at the various
stability categories then become apparent. Figures 14 through 29 are
frequency and scatter plots of the eight stability classes, and Table 12 is a
summary of these 16 figures. In Table 12, with the exception of class AB, the
observed averages are ranked in descending order from class A through class E.
This order is reasonable since the highest S02 concentrations are found in the
most unstable situations. However, the Gaussian plume model does not
completely follow this ranking. This model has the highest average at class C
instead of A, and the averages do not descend nicely as do the observed
averages.
The frequency distributions of the calculated and observed values for
class A stability are close to one another (Figure 14); however, the model
tends to grossly underpredict for SC>2 concentrations greater than 100 yg/m^.
The scatter plot reveals a majority of the points within a factor of 2 and
many points with very good agreement (Figure 15). The tendency of the points
to be skewed to the right for high concentrations shows the model's
incapability to predict high concentrations of this stability class. This
could be the result of the sector-averaging technique.
The frequency plot for occurrences in class AB stability shows remarkable
overall agreement between the observed and calculated values for S02
concentrations (Figure 16), although the calculated values are slight
underpredictions. The scatter plot shows 57% of the data points within the
set error bounds (Figure 17).
Although the concentration ratio is close for class B, and 67% of the
points are within a factor of 2, the frequency distribution curve of the
calculated values has a different shape from that of the observed values
(Figure 18). This, again, is a general tendency of the Gaussian model for
this particular stability class. This same general tendency is observed in
the frequency plot for class BC (Figure 20). The model tends to overpredict
(Text continues on p. 54)
-34-
-------
^ O> «>
^ w °*!
II II II
TJ -a o
O
o>
O)
O
O
CM
O
CO
O
O ^
O O
!N30H3d
8
Nl
iJ
4-1
•§>
01
C!
O
0)
o
o
a
CSl
I
cu
CO
o
T)
CU
u
-, cu
o a
d C
cu cu
3 n
cr 3
Q) O
l-i O
Pn O
CN
60
-35-
-------
\
\
\ °
\ x
x £
\ II
\ "
u
\X
\
\
\
\
\
0 \
X \
^ \
» \
J> V
xX \
\ \
\
\ \
\
\ \
\ \
N \
x *
X \
\ \
\ \
x \
\ \
\ x
X. \ \ '
CM \ '
^ \ \
II \ \
0 \ >
X \
x X
\ ' '
^ \
X
^ x \ *
•»_ *
N \
x \ •
v. x \
~"^ x \ ;
X. V .
\ \ •'''
^ \ • . x .
^ v •* •.
\ * :
• \ . N ^
^^ : «>x*-\ Li
^* N \'«t
-x \ \2
-v ^ X
X^\\
^4
rO
CM
O
CO
O
CD
O
•^*
^i
O
CM
O
O
0
CO
O
CD
_O
^»-
^sl
o
CM
666666
CM O CO CD ^ CM
/ • • • / a ^fi \ *>
(cLU/D/7l SUOI16JlU93UOn D91BinOIB/^ = 3Y
'~~~m
n
E
•>„
O)
=t
fi\
v)
c
o
•*-*
cu
k.
-!-•
c
CD
O
C
0
O
•o
0)
^1
0>
/A
U)
n
0
H
1 1
o
X
M
O
U-l
CO
c
o
•1-1
[ 1
cfl
J^
4J
c
0)
o
a
o
0
CNI
o
CO
T3
0)
>
J-4
0)
CO
^2
O
T3
C
ca
T3
0)
4-1
cfl
iH
3
O
.-1
tt)
0
4-J
o
en
4-1
.5
0
ft
ctf
4-1
n)
rr-t
W
>* •
t-i CO
(-1 01
3 0
0 C
,fi CU
M
U-l }-l
0 3
0
4-1 O
0 0
a. o>
S
M -H
01 4J
4-1 4-1
11 f-t
4J ,C
cti 60
0 -H
W C
01
, — 1
0)
t-i
3
60
-36-
-------
E E
O
O
CN
O
CO
o
co ^
DC
S H
9 z
^ LU
O
z
o o
00 O
•88
o
CM
•H
iH
•H
&
cd
4J
CD
cn
en
to
o
M-l
o
-H
c
01
o
ti
o
o
CM
O
T)
0)
QJ
tn
0)
4-1
cd
n)
o
U-l
o
cn
C
O
•H
•H
M
4J
CO
•H •
•n CD
0)
>, o
o c
ti a)
0) (-1
CD O
VJ a
fn O
O
in
o
CO
o
CM
!N30H3d Nl
3
60
•H
-37-
-------
r
\
\ °
\ x
\
\ I'
\
u
\ X
\
\
\
• .
\
\
\
o • \
x \
'I \
o \
X x
\ \
\ \
\ \
X \
X \
\
\ \
N \
\
\ \
X \
\ .
\
\ x
X°
CM \ • \
II \ \
X ' ^ \-
^^ ' \^ \
^ ^ x \
^-, ' \ \
^^ ^ \
. ^ . . \ \
"^ N \
^ >v X \
• ^ \
^ ^ . *\ • \
N\ ^ -
Nv » \
"•v X \
• v>» \ \
* ^ »^ \
^ V
^ s \
v^
CM
O
-00
*~
O
-co
o
^J
^^
o
CM
0
•0
o
"00
o
CD
.O
Mfc
.O
CM
666666
CM O CO CD t CM
T"" ^m
— _ _i
(CLU/6T/) suoiiBJiuaouoo pe^BmoiBO = °Y
f" K
n
E
D)
*•*.
U)
c
o
^
*c
(D
u
c
o
o
"§
-
(A
o
II
o
X
Jj
o
y-i
M
c
o
4J
cfl
J-4
4-1
c
QJ
0
t3
o
a
CN
0
CO
-a
>
QJ
CO
o
•a
CO
•a
01
CO
t-H
3
O
1 — 1
CO
o
,.
o
03
C
•H
O •
a, en
-t
>^ 3
•-I O
V4 0
3 O
0
4-1
i^ i ,_j
O -H
•H
4J pO
O cO
rH 4-1
CU 03
0)
4-1 CO
4J 03
CO CO
O iH
CO O
^
0)
3
bO
•H
-38-
-------
E E
O
O
CM
O
CO
O
(D
—
O)
O
CM
O
O
O
00
O
(O
DC
H
Z
LU
O
z
o
o
o
CM
0
m
i
o
Ti-
o
CO
o
CM
I
0
Nl
>.
4-1
•H
iH
•H
cd
4J
cn
pp
cn
en
cfl
1-1
o
tn
e
o
•H
4-1
CO
S-i
4-1
C
0)
o
c
o
o
CN
o
T)
0)
n
0)
en
-a
c
n)
"S
4-1
03
^H
3
0
rH
cfl
O
en
(3
O
•H
4-1
cn
•H
xt cn
0)
>s O
O (3
S eu
0) J-J
co o
l-i O
Pn O
0)
l-i
3
60
•H
-39-
-------
\ °
\
\
\
\ °
\ X
x <;
\ Ti
\ "
• 0
\ X
\
\
\
\
\ -
\ •
\
0 \
x \
II \
o >
X N
\ . \
\ \
x \
\ x
\ \
*X
\ \
N •
\ \
^ \
\ ••
\ • \ •
\ * ' \
. , •
• \
\ \
o xs \
X * \ \
CM Nx. \
II N \
X \
X \
.-v N \
N . \
^ N \
x \.
x . \ •
^ \ • .
•^ . •• • x
>^ ^
^ • \ \
^ \
*x \
^ • x \
^^ «, *\
^- . ' X v
v X • '*
X , \
• x % .. \
• ^^ • ^ \
• ^ v?
. » . • ^ v • «»^ \
\^ • \ \*
*-" .. \ \*
. .^ ' • . *^. r
*^ >.• x ,
"* ^ \ \
^ N \
^i
ro
CNJ
o
CO
o
-CD
o
-^
^1
o
CM
T~
o
-0
o
CO
o
"CD
O
vf
>J
o
r\.t
\^4
666666
CM O CO CD ^ CM
(tuj/67/) suonejjueouoQ pa;e|no|BQ =°x
CO
E
^>.
D)
3.
(A
C
0
^J
CD
+-i
C
0)
o
c
o
o
TJ
k_
0)
tf\
(/}
0
^•M
O
II
1 1
0
X
CO
CO
Cfl
i-H
0
M
O
4-1
CD
C
o
•H
CO
te
4J
C
i O
r-H C
(-1 0)
3 H
0 ^
,C 3
O
>4H O
0 0
4J ^
O 4-1
rH -rl
P^
M J2
a) cfl
4-1 4J
4-1 W
Cfl
a PQ
CO <
i —
, j
cu
t-i
3
60
•H
-40-
-------
E E
O
O
CM
O
00
0 o
CM >£
O
z
o O
00 O
o
CM
000
in ^ co
!N30H3d Nl
o
CM
•H
i-H
•H
,£>
cd
4-1
M
to
to
TO
C
o
•H
c
0)
O
O
a
CM
O
C/3
S-i
(U
cn
,0
o
13
•8
3
o
CO
O
CO
(3
O
•H
4J
3
CO
•H •
13 W
0)
>^ O
O C
a
-------
. o
N • !-<">
\
\
\ °
\ »
\ *r
\x°
\
\
\
\
\
\
0 \
x \
II .\
o • \
xX \
\ \
X V
\ ' \
X \ * *
\ \
X \
x • *
\ •
\ \
\ \
0 \. . \
x \ .• x
CM \ • . \
II \ . \
0 \ \
*u* v • ^ •
>* : \ • -\ •
^^ • \- . x
^ ^ X *
\
^ ^ V \
*- * x \
^ -X
"~ •• • \ \
^ • * X • V
**• • X v
^ X* . \
. ** ^ \ *
•s>.. •»' \
.... ^^ x N
"">>.*** X* X
^ ^ . \ X \
•. ^-^ • v \
• • • ^ x\
-^ X \
CM
0
CO
O
to
*~
0
1—
o
CM
0
o
o
co
o
(0
.0
"^J
.0
CM
666666
CM O CO UD ^ CM
^™ ^^
(eUJ/B7/) SUOUBJ^UaOUOQ pOJB|nO|BQ =°X
rt
E
Of)
^«-
-.
c
g
"CD
~^
c
0)
o
c
o
O
0)
0)
f)
O
II
o
X
CO
CO
CO
o
o
CO
c
o
•H
4-1
cfl
t-l
4-1
C
01
o
c
0
o
cf0
C/D
T3
01
M
01
CO
O
C
cfl
13
CU
4J
cfl
rH
D
a
cfl
O
0
CO
4-1
C
•H
o
a,
nl
4J
CO
f>s 01
rH O
3 0)
O V-i
rC U
3
m o
o o
o
4-1
0 >.
i~H 1 '
a. -H
i — I
M *H
0) ,0
4-1 Cfl
4-1 4-1
Cd CO
O
ON
rH
0)
3
M
•H
-42-
-------
O
O
CM
O
CO
o
(O ^
T- to
E
0
CM
2 »-
o z
^ LU
O
z
o o
CO O
O
(O
o o
*• co
lN30H3d Nl
o
CM
•H
•H
CS
4-1
CO
O
PQ
CO
CO
rd
o
U-l
CO
(3
o
•rl
4-1
c
0)
o
e
o
o
CM
O
c/)
13
CD
t-4
CU
CO
cu
4-1
cd
3
O
cd
a
CO
(3
o
CO
•H •
T3 CO
OJ
>, O
O C
C CU
0) l-l
3 H
cr 3
cu o
VJ O
p^ o
o
CM
0)
S-i
&0
•H
-43-
-------
\ o
\
\
\ °
^^
\ 5
N Ti
\ "
u
\X
\
\
\
\
\
\
0 \
x \
II \
X° N
\ \
\ \
\ \
\ x
V
\ \
N \
\
\ • \
^ \
\
\ \
\ * \
^ . \
\ X
N
X \ \
•M \ \
II N \
0 \ \
X \ \
^ \ . \
^ \
"- • x \
x \
N • \
^ . N ,
N . \
^ \ \
"- N \
N \
^ \ \
^^ x \
' • \ ^
' • "^ \ \
* \ \
X \
• v \
>. ^N X
-x . X \
^-. x \
^ s \
"^ N X
^^ X \
^i
"O
CM
O
-co
MHB
T
o
CO
O
-^
^i
o
CM
^
O
o
0
CO
ro
co
o
"-*.
^^
o
CM
O 0 0 0 O O
CM O CO CO ^t CM
(Eui/67/) suoijej^ueouoo p9^B|no|eo =°x
^ *
n
£
O)
-v
-a.
(0
c
o
^^
ffm
Cw
u.
^^
C
CU
u
c
o
o
T3
0)
>
v_
(1)
(/)
^J
O
II
II
o
X
CO
CO
cd
rH
O
)-l
o
t[ 1
en
a
o
>rH
4-1
cd
!H
4J
CU
0
c
0
O
CM
O
C/2
T3
CU
>>
5
CU
CO
&
0
T)
C
cd
13
cu
4-1
cd
3
O
rH
cd
o
4H
0
CO
4-1
•H
0
O,
cd
4-J
cd •
13 CO
CU
>-, 0
rH C!
JH cu
3 H
O M
J3 3
0
U-4 U
0 0
•u >i
O 4-1
rH -H
O, rH
•H
^-1 .'"'
cu cd
4-1 4-1
4J CO
cd
o u
co m
rH
CM
cu
M
60
•H
-44-
-------
E E
•H
,0
Cfl
to
en
S-J
o
4-1
W
c
o
•1-1
4-1
to
c
0)
o
c
o
a
CNl
(-1
0)
0)
4J
n)
n)
a
14-1
o
o
•H
4-1
3
4-1
W
•H •
T3 W
0)
>, O
O C
3 ^
a- 3
a; o
)-l O
PLI O
cs
(N
Nl
60
•H
-45-
-------
. o
\ ^
\
\
\ °
x
\ 5
\ 7.
\ "
\
U
\ x
\
\
\
\
\
\
\
\
0 \
\
«.
\
o x
x* \
* v \
x \
\ x
\ \
x \
\ . \
\ • • \
. V
X V
\ x
\
x x v
N \ \
II \ • \
0 • \ >
X x* * \
"•• x \
^ . ' • \-
»v • X •
•^ • x \
\
\
x \
-» N.
X . \
* "*»•.» v * \.
• . ^- N ^
• • "v. .X \
x-i • \ \
• ^ >
^-v • X \
• . . ^v X \
* • * .' tv*^. X.
• ^ X * X
* -v V \
• * ' • ^>. S \
. . ». >» v X
"- \ \
^:-\
^i
0
CM
O
-00
0
-CD
O
-^
l1^
O
^-CM
O
O
O
CO
O
CD
O
<^
.O
CM
666666
CM O CO
CO
4-1
oncen
O
•o
fit
w
•>
l_
1-1
0)
CO
,0
o
13
§
Iculated
cd
o
U-4
O
CO
4-J
5
data po:
rrences.
t^ 3
i-H O
^ O
3 0
o
43 ^
4J
4-1 -H
O TH
•H
4-" 43
O cfl
iH 4J
CX CO
* U
0)
4-1 CO
4J CO
CO CO
O -H
C« U
CO
CM
2
3
60
•H
Pn
-46-
-------
to <<>
E E
O> B>
(0 rt
O
a>
•O -D O
fl) fli —
*- > «
re 4. «"
— Q) OC
o w
re O
O
o>
o> o>
2* *
re Q)
5 <
O
if)
O
CO
o
CM
Nl
p
o
en
en
cs
CO
c
o
4J
d
8
d
o
a
CN
O
en
13
a>
a)
CO
,0
O
13
OJ
O
H
CS
a
CO
g
4J
CO
•H .
13 CO
0)
>> O
O C
pj CU
a) o
M O
P4 O
cs
cu
60
•H
Fu
-47-
-------
0
\
\
\
\ °
\ x
\ ^
x Ti
\ "
o
\ X
\
\
\
\
\
\
0 \
X \
H \
11 \
0 \
xX
X \
X \
X X
\ \
x \
\
X \
x \ .
\
X \
\ \
\ \
x x
x x
X
X \ ' \
CM \ \
II X» \
0 X X
X x . .
^ \ \
N
X
^ x \
^ x .
^ X X
^ • V
^ x \
x \
. ^ ^ X \
X \
x \
. ^ •• \ \
•-V \
*•"" \
• ^ X x
* • • ^^ x \
N \
• ^ • N.
• '. %- \ x
.' .. . . ^ \ \
x \
x
- \ \
^-*t
o
CM
0
-co
^~
o
CD
o
rf
o
CM
O
0
o
CO
0
CD
o
Tt
.0
CM
6 6 6 6 6 6
CM O CO CD ^ CM
co
O)
-w
••-*.
^^^
C/5
\ff
c
o
+^
n
^.
^->
c
0)
o
c
o
o
•o
0)
k_
0)
0)
^
0
II
o
X
S-i
o
M-l
CO
c
0
•H
4-1
ca
t-i
4-J
c
QJ
CJ
a
o
o
CN
O
CO
13
0)
t
0)
CO
,0
0
-d
S
ca
T3
Q)
4J
ca
, — i
a
iH
C3
O
M-l
O
CO
4-1
C
•i-l .
o co
CX 0)
a
ca c
4-) CU
« M
13 f-i
3
>. 0
,-1 0
3 °
0 >,
J2 4J
•H
^ ,H
O -H
J3
4.) C8
O 4-1
t-H CO
CX
P
H U
01
4-1 CO
4J CO
ca ca
a I-H
w o
in
CN
0)
u
(ctu/67/)
-48-
-------
E E
00
o>
CO
,- in
T- CM
T3 "O O
0) 0) ~
** > —
I S
a O
O
o>
(0 Q)
ro
O
v-
0 0
in ^r
• -^
i
o
CO
0
CM
—
~~~ "~ "* — • —.
0
o
o
CM
O
CO
o
<£> ^
^"" «
o
cc
2 H
o z
^ LU
O
z
O O
CO O
o
CM
en
O
en
to
g
•rl
a
s
§
o
0)
fc
0)
en
o
T3
•O
QJ
rt
iH
3
cd
u
4-1
O
J-i
4J
05
Nl
to
a*
C a)
-------
\ r§
\
\
\ x°
\ -^
\ *"
\
o
\ X
\
\
\
*
\
\
\
0 \
X \
II \
o
X x
\ \
\x \
\ \
\ \
x \
\
\ \
x \
\
x \
X V
\ x
\
2 - \
04 \ \
II \ \
0 \ v
X ^ \
\ -
\
^^. ^ \
x \
\
^
>. \ • \ • •
^>. X \
^ ^ \ \
\ \ :•
\ A • •
"^-^ .SN . \ .*.!
^-^•r 'V»\ --.
. * V \ * *
^"-" ^\ \
^"^ x \
CM
O
CO
^M
O
~(0
^•B
0
^—
o
CM
o
0
0
CO
o
(O
.0
fM^
"
.0
CM
O O O 0 0 0
P4 O CO (D ^ ?*
• ^>
i t C ^9 \ *\
IrvLU / D/7 i SUOIlBJlUdOUO^) DOlBiriOIB'^ ^^ Y
^^
m
E
O)
^5.
CO
c
g
03
^".
c
CD
O
C
o
0
•o
^ 0)
rH O
3 §
O M
3
M-J O
O U
O
4-1
O $>*,
i — 1 4J
CX -H
M -H
QJ ,n
4-) Cfl
4-J 4-1
Cfl 03
O
CO P
CN
a)
S-i
3
M
•H
-50-
-------
<•> n
E E
O) O) oo
CM
II II
O
•-
~
n
tt
0) 0) •-
*- > ~
— a>
I 5
m O
O
a>
a\ O)
S. •
(0 A)
>
<
O
O
CM
O
00
O
CD -^
O W
^ =1
0 O
CM i£
O
O
<
DC
LJJ
O
O O
CO O
O
(O
•8
03
4-1
CO
CO
CO
Cfl
V-l
O
4-J
O
•H
4-J
0)
s-l
4-1
c
0)
O
c
O
O
CM
O
CO
0)
CO
,£>
O
T)
O
rH
cfl
a
O
CO
o
•H
4-1
•H
4-1
CO
•H
CO
CU
3
o
O
O
lN30H3d Nl
00
g
W)
•H
-51-
-------
\ °
\
\
\
\ °
\ x
\ ,£
x T,
\
u
\ X
\
\
\
\
\
\
0 \
x \
II \
o x
X X
\ \
\ \
X V
\ x
\ \
x \
\
\ \
x \
\
\ \
\ \
\ \
\ \
\
2 N \
01 \ \
II N V
0 X \
X \ N
x x
^ x \
x
^ \ \
^ \ N
^ X \
^
^ \
•^ \
^ X V
^ \ \
N \
^ ^ X \
v. \ X
\ \
~-^ \ \ '
•V
v \ \
v V X .'
^ X \
X X i
^^ N \ -
^ X \ ~
^^ x •
^^ \ x
^*\
rO
CM
0
-co
T
O
-co
r~
0
^
o
CM
O
o
0
CO
o
(O
o
"^t
XI
.0
CM
666666
CM O CO
S-i
OJ
CO
/2
O
T3
3
T3
01
4-J
CO
rH
O
i— 1
CO
O
I L I
"-M
0
CO
4-J
•H
O •
CX CO
0)
cfl O
•U C
CO 0)
T3 *J
}-j
f-, 3
.—I CJ
M O
3 0
0
J2 i^
4-1
MH -H
0 T-H
•iH
4J .<*»
O cO
iH 4J
a co
!-i W
0)
•U Cfl
4-1 CO
CO CO
CJ rH
cn cj
C^
rxt
CU
S-i
3
60
•H
-52-
-------
>H
£— (
M
M
OJPQ
O <
CO EH
CO
Q
W CJ
> M
cd w
CO DC
P3 OL,
O CO
O
0 S
2 H
^j ^1
O Cn
Cd O
< co
J Cd
D co
cj co
.J <
CJ CJ
Ci-. O
0 H
CO O
M 2
CO M
>H Q
,J K
(D
5- O -P >
-P -rH Cfl t.
C -P ^H CD
CD Cd 3 M
O £H O £>
C rH O
O cd
cj o
c
C\J O
O -H
CO CO -P ^
ho cdoo
Cd T3 SH E
5_ 0) -P \
CD > C bO
> t. 0) ^.
<$ 0) O ^^
CO C
jO O
O 0
OJ C
0 0
CO -H
-P ^
3 0 ^
•a; o c
rH O
cd o
o
cd
c
•rH
.C OJ
•0 -P
C -H CM
cd 5 O
£-, CP t,
CD bC O
Xi cd -P
E -P 0
3 C Cd
2 CD CM
o
CD
G.
CM
o co
•O CU CM
g CD o c^
cd bo c -=r
cd CD
5-, -P c_ CM
CD C f-, 0
XI CD 3
S O O -P
3 S-, 0 3
2 O> O O
D.
•H CO
rH CO
•H Cd
XI i— 1
cd o
-p
CO
CO T- O O
t— c» t— oo
OO OO OO CT*
• • • •
o o o o
oj c —
-------
up to a certain concentration level and then underpredicts the highest
concentrations.
For stability classes C and CD the model shows an inclination to grossly
overpredict. The frequency distributions of these stability classes are
skewed to the right with respect to the observed values (Figure 22, Figure 24)
The scatter plots of classes C and CD show 41$ and 31$ of the points within a
factor of 2 (Figure 23, Figure 25). A majority of the points lie within the
upper 22.5-deg sector, which illustrates the model's overprediction for these
two stability categories.
The last two categories, D and fi, are grossly underpredicted by the
Gaussian plume model (Figures 26-29). Class fi is by far the worst with a
concentration ratio of only 0.0188. However, only those hours that registered
more than 10yg/m3 at the monitoring site were selected.
Discussion of the model by stability classes raises many questions. The
most sensitive parameters in the Gaussian model are the dispersion
coefficients, which are functions of downwind distance x and the atmospheric
stability. The largest variance comes from the different stability
classifications. In other words, the most sensitive of all parameters
associated with the Gaussian plume model is the selection of stability.
In reviewing the scatter plot of all the data points (Figure 11), it was
questioned whether or not the scatter was truly random. A portion of the
scatter was attributable to nighttime occurrences, with the model
underpredicting the observed values by a factor of 5. Nighttime atmospheric
stability is either neutral or stable; it can never be unstable because no
atmospheric heating due to solar radiation occurs at night. Therefore, the
underprediction at night is attributable to the model's tendency to
undercalculate for stability classes D and fi (neutral and stable) . The
underprediction of classes D and fi is also not random; the scatter plots show
definite clusters of points (Figure 27, Figure 29). In the cases where the
model overpredicted, namely classes C and CD, it did so consistently, as shown
in the scatter plots for these classes (Figure 23, Figure 25).
The four stability classifications A, AB, B, and BC had an average
concentration ratio (ratio of calculated value to observed value) of 0.885 and
had an average 64$ of tne data points within a factor of 2. This percentage
is much higher than the 48$ calculated for all of the classes combined. Some
of the scatter in these four cases is possibly due to incorrect
stability-class designation.
Calms and Periods of Rainfall
Another pronounced flaw of the Gaussian model is its inability to predict
accurately during periods of low wind velocity. As the wind approaches a calm
(a wind speed < 1 m/sec was the cutoff for a calm), the effective stack height
may be greater than the assumed mixing height of 1,500 m, in which case the
calculated ground-level concentration is zero. When wind speeds are low but
not calm, plume rise will nevertheless be large, making the predicted
ground-level concentration lower than possibly would be observed. This was
-54-
-------
the case for several of the observed data points in Figure 11. As a general
rule, the model's incapability of accurately calculating high concentrations
during low wind speeds caused the model to underpredict.
Some of the scatter in the plots was thought to be caused by periods of
rainfall. During rainfall some of the S02 is washed out of the air. Rainfall
was recorded during 13 of the 492 h when the plume was there. During rainfall
the average calculated value of S02 was 30 vjg/m3, and the average observed
value was 40 Mg/m3. Apparently some of the SC>2 in the air is washed out by
the action of tne rain.
AVERAGE CONCENTRATIONS AT THE SEVEN MONITORING SITES
The annual average concentrations at each of the seven S02 monitoring
sites were examined. Figures 30 through 43 are frequency distributions and
scatter plots of the data observed at the seven monitoring sites; Table 13 is
a summary of tne results. All of the concentration ratios are close to 1, and
the model is therefore consistent in predicting relatively accurate annual
average values. A correlation coefficient of 0.954 was calculated based on
the annual averages for the seven S02 monitoring stations. The annual
averages are shown as a scatter plot in Figure 44.
The sites were ranked in order of highest to lowest S02 concentration, and
the concentrations were compared with the distance from the stack (Table 14).
The highest average observed concentrations as well as the highest average
calculated concentrations occurred at the sites nearest the stack, and the
lowest concentrations occurred at the sites farthest away from the stack.
The highest concentration ratio, 1.24, was observed at the Messer site.
The Messer site is the only one of the seven that is located in uneven
terrain, namely, the Baraboo Bluffs. The increased turbulence caused by the
hills could very well explain the highest concentration ratio. The two lowest
concentration ratios, 0.741 and 0.788, were calculated for the Bernander and
Hussell sites, respectively. These two sites are farthest from the generating
station. Higher concentrations would be expected at these stations during
periods of neutral or slightly stable atmospheric conditions. Neutral or
slightly stable conditions prevailed 73.9$ of the time at Bernander and 63.2$
of the time at Russell. The highest percentage of occurrences for stability
classes D and E, coupled with the model's tendency to underpredict at these
stabilities, explains the two lowest concentration ratios.
WORST-CASE OR HIGHEST S02 CONCENTRATIONS
The worst-case or highest observed hourly 302 concentration due to the
generating station during 1976 (with the background removed) was 247yg/m3,
and the highest model output was 157yg/m3 for the sector-averaged value,
which corresponds to a plume centerline of 245yg/m3. The highest observed
and calculated values did not occur at the same time, however. The high
concentrations occurred with A and AB stability conditions. The highest
observed concentrations were not associated with inversion-breakup or
fumigation-type conditions in this study. (Text continues on p.72 ).
-55-
-------
CO ft
E E
^- ~-»
O) O)
N^ ^ O
ir> ui 9
CO CO r-
•o -o
0) 0>
** >
m Z.
— o>
« JS
ra O
O
o>
m O)
(0 4)
• I
4J
cd
cn
cu
u
d
cu
S-i
n
o
o
o
M
O
cn
C
O
•rl
4J
00
01
u
g
o
CM
O
CO
0)
CO
0) ^<
J-l CM
Cd O
r-H O
3
CJ 0)
r-H 4-1
n3 iH
o en
O CD
CO -H
ti cn
4-1 J-l
^1 Q)
f 4-1
•H QJ
^ e
4-1 cu
cn o
•H
13 0)
t>o
!>i cfl
O 4-1
(3 5-1
O
0)
cr
a)
O
ro
0)
PL,
lN30H3d Nl
00
•H
-56-
-------
o
\ ro
\
\
\ °
x
\ *~
\ "
u
\ X
\
\
\
\
\
\
0 \
x \
II \
o ,
X x
\ \
\ \
\ x
\ \
\
\ \
^ * \
\
X
\ \
\ \
\ .
\ \
2°
\ \
II \ \
0 \ \
v.X \' * \ f '
^- X\ X*
^^ X\ \
X \
\
• v y
^ \ \
X .
^ • \ \ •
\ \
• ^ x \
^- \
^i \ \
• ^ ^ • \ A *
^ \ • *
*. *^^ *N.\
* •* .^^ \*\.
^ ^ x \
^^ \ \
CN
O
-co
O
-CD
r"
O
r-
O
-CM
O
-0
o
CO
0
CD
.O
_^
.O
CM
666666
CM O CO CD ^t CM
^^ ^^
(euJ/67/) suo.;Bj;ueouoo we\no\eo=*x
co
E
D)
3.
w
c
o
CO
"c
r^-,
0)
S-l
0)
CO
0
••o .
S CN
0
'O ^^
cu
4-1 CU
CO 4-1
S en
o ^
rH
CO 0)
a 4J
•H
<4H CO
O
CO >-l
4-1 QJ
cd i '
•H cu
o B
& cu
o
cO
4-1 CU
CO 60
T3 CO
4J
t^i r*
rH O
SH P-,
o cu
r^ f~f
4-1
M-l
O 4J
cd
4-1
O CO
rH CU
ft a
C!
>-i a)
cu s-i
4-1 (-J
4J 3
cd a
0 0
C/3 O
rH
ro
CU
M
•H
-57-
-------
co to
£ E
I I 8
<6 r«»' °i
\\ \\ \\
TJ O
0)
m >
(0 w
"a *
I 2
co O
O
0>
m O)
5 S
s <
CD
CC.
O
O
CM
O
hCO
O
CO ^
^ m
o
CM
o
o
o
00
Z
LLJ
O
O
o
o
CM
to
d)
o
G
O
O
o
(-1
o
M-l
CO
c
o
•H
4-1
4-1
C
(U
o
c
o
o
Q)
M
CU
CO
cu
4J •
n) ^
rH CO
3 O
O O
rH
Cfl CU
O 4-J
•H
4-1 CO
O "-X
cn cu
d 4J
O -H
•H CO
4-J
3
&
•H
(1)
60
CO O
•H
T3 0)
,3
O
it)
O
CO
o
CM
O i-J
d
CD cu
cr 4-1
CN
CO
lN30d3d Nl
00
•H
-58-
-------
0
\ . o
\
\
\ °
x
\ *
X
2
te
^^j
^^*
c
^m
0)
(/)
12
O
0
X
M
0
m
CO
c
o
•H
4-1
cd
M
4-1
C
QJ
O
C
o
u
s
w
T3
CU
£
cu
to
,0
0
T3
c
cd
T3
a) .
4J /-^
cd on
rH O
3 0
0
rH 0)
td 4J
0 -H
co
14-t ^^
o
0)
CO 4-1
4J -rl
C CO
•H
O 0)
CX 60
J-J
ca o
4J 01
cfl O
T3
0)
>1 ^
in ca
^ ij
O 0)
J3 X
4-1
M-l
0 4J
n)
4->
O CO
, ) Q)
cx o
d
^ 01
0) )-l
4J H
4-1 3
cd o
CJ U
OT O
•
on
on
(jUi/67/) suo^ejjuaouoQ pajB|no|BO=°x
60
-59-
-------
E E
O
O
eg
O
-co
O
-co
o
CO
o
CN
o
CM
o
o
o
"00
co
o
H
LLJ
o
z
o
o
C7)
o
'CM
co
0)
o
d
0)
M
a
o
o
o
M-l
M
a
o
•H
4J
n)
OJ
o
a
o
o
-l 4-1
4-1 -H
CO CO
•H
T3 td
M
C A!
cu cu
3 Q
cr
cu cu
Nl AON3nO3dd
2
60
•H
-60-
-------
o
. * • •
\
\
\ °
\ *
\ "~
\ "
• o
\ X
\
\
\
\
\
\
0 ' \
X \
II \
o . v
x*. '• \
\ \
\ ,
\ . x
\ \
X \
'. «\
\ • . • \
\ '..
\ \
\ a\
N . •* \
\ * X
0 \. • \
X' \ . x
04 v • \
II X ' \
o • x \
X \ \
'"^ * ' \- . • X
^ . • \ x
^ x \
^ .\ >
"^ * ' \ \
^^ \ \
V. * *\
^ >. •• : \ \
"^ ^ \ \
' .' x \
. .• ^ \
x \
X \
. r v x \ .
• • "* ~~ ^ x «
^ ^ X \ "
^ N x
^^>. \ x
~" ^ x N
^ ^ \
^
rO
CM
0
CO
^^
0
-CD
T~
O
T~
O
CM
O
o
o
"CO
o
"CD
.O
^+
.O
CM
O O O O O O
CM O 00 CD ^ CM
^^ ^™
(eui/67/) suoijej^ueouoo pajeinoieo -^y
^_^
m
E
O)
^"^
^*t
^•^
f/i
c
o
03
^_,
C
0
o
c
o
o
^•B
0)
^
0)
\if
^^J
o
II
o
X
!_,
o
to
a
o
•H
1 J
M
c
0)
o
c
o
o
o"1
00
T)
>
0)
Cfl
•§
13
c
cfl
4J
rt
1
r~1
^ *
O /-x
rH -vl-
03 o
O O
m o)
O 4-1
•H
co cn
pi
•H 0)
O 4J
O, -H
CO
03
03 }-4
13 )-(
O
f 3
O 0)
4J
MH
° tti
4-1
O CO
i— 1 CD
P. a
(3
>-i a)
dJ M
1 1 < |
4-> 13
n) o
o o
oo o
LO
ro
a)
3
60
•H
-61-
-------
n n
E E
CO
—
** > **
n ^ «
•= o> QC
3 m
O
0)
>
<
o>
O
O
CNJ
O
00
E
o
CM
^ »-
S
LU
O
O O
00 O
o
CsJ
O
in
o
^t
o
CO
0
CM
0
Nl
CO
0)
o
C
O
CO O
c
O Ol
•H 4->
4-1 -H
S CO
>-i 0)
4-1 4-1
CO -H
•H CO
-a
M
>, 0)
o co
d to
cr
0) 01
ro
CU
S-i
M
•H
-62-
-------
o
\ . ro
\
\
X x°
\ ^
\ Tj"
\ "
o
\ X
\
\
\
\
\
\
0 \
X \
II \
*
\ \
\ ,
\ N
\ \
x \
\ \
\ x
\ \
N \
\
\ \
\ \
\
*l N \
^ \ \ ,
II \ \
0 \ \
X \ \
^- ' ^ \
^ X X
^ \ \
^ v N
X
•v \ \
\ ^
*^ \
^v "\
^^^ X \
^^ . x \
^ \
. \ . \
» "^ • *• \. \
•^"^, XNX \
• .".^ • \ \
^ ^ \ \
^"-^ Ns \
CM
O
-00
^~
O
-co
*~~
o
^
o
-CM
0
-o
0
00
0
CO
.0
^
.0
CM
666666
CM O 00 CO ^ CM
^^
m
E
0)
^>
W)
c
0
03
^—
c
0)
o
c
o
o
-i
13 0)
to
>-, to
O
-------
co n
*•? CO
in
T3 TJ O
d) fli *^"
~ > m
(0 w "*
^ 0) DC
o ^
to O
O
o>
O
O
CN
O
CO
O
CO ^
o
CM
O
O
<
DC
LU
O
O O
CO O
O
'CM
o
CM
4-J
nj
CO
CU
o
fi
CU
O
a
o
M
o
o
•H
4-J
cfl
M
4-1
fi
-------
0
* •
\
\
\ *
\ ^
\ "
o
\ X
\
\
\
\
\ •
\
0 \
X \
II \
0 x
x*
• \ \
\ V
\ x
\ \
X \
\
\ \
\ V
\
\ \
\ * \
* «
\ \
g° "^ \ •'
CM \ \
II \ \
0 \ v '
X \ \ .
^ \
\- >
^ N • , .
^ N . . \
- N * • \
^ V • *
\ \
\ \ -
^ \ *• *\
"^ . \. . **
^""^ * * *\ ". X
^ ^ * \ • •• \ *
.••""-• • X'."* \
r^y;*>\*..\
* • . • "^w » y*» \
* *•• ^ ^ \
^^\\
ro
CM
O
CO
^™
0
CO
*~
o
^
0
CM
^~
O
-o
o
CO
o
"co
.0
.0
CM
o o o o o o
CM O CO CO ^ CM
^^
n
O)
3.
(A
C
o
"tn
jy
^_j
c
CD
O
C
o
0
•o
CO
^
o
CO
o
II
0
X
o
M-l
(0
o
•H
4-1
cfl
t-l
4->
g
0
c
0
o
CN
W
'G
Q)
S-l
0)
CO
J3
0
id
n)
0)
4J
d
o ^,
rH 00
tfl 0
O O
m a;
0 4-1
•H
w en
4-1 ****
C
•H 0)
0 4-1
a -H
«5
cd
nj o
T3 -H
£g
M C)
3
0 (1)
4-1
O 4-1
nj
o en
H Q)
cx a
in a)
a) >-<
4-1 ^4
4-1 ^
IB O
O U
WO O
C^i
cu
(cui/67/) suoiiBJjuaouoo pa^B|no|BO =°x
a
60
-65-
-------
n
£
O)
M
3>
,
T~
¥
•Q
0)
*•«
re
o
"re
0
0)
0)
re
0)
<
T3
Q)
•+-*
CO
-.
o
CO
O
\
1 -
11 , *..
*~— — -^
O
m
n
E
"^ a>
2 o
X •*
•
¥ n
•Q O
^ re
Q) OC
(A
13
O
0)
re
0)
<
"O
(D
^
0
co
_o
o ^ ----
V—— •*"* "*-* ^***^^
^~ —~'~_^^^^
ff — — —
__- _^ —~— — — ^
— - — — — — - - — ^
~^" ~— — - _^_
0 O O 0
^ CO CM T-
0
rO
CM
0
-co
*~
o
-co
o
'^
1~
o
-CM
*"
o
o
^
o
00
o
CO
_o
^y
o
"CM
^_^
n
^x
O)
5*.
s— "
o
h-
DC
H
Z
LJJ
O
z
o
o
CM
0
0)
4-1
cd
CO
CO
a
e
co
h
3
o
o
0
o
M
O
•H
4J
n)
4J
C
co
a
5
o
o
"
M
co
CO
o
"c
n)
a)
4-1
i — i
3
0
rH •
Cfl ,~^
O ON
0
4-1 O
O
ai
CO 4-1
C -H
O CO
4J
3 0)
r*i [ j
•H -H
1-1 CO
4J
cn !-i
•H CO
T3 T3
o a
e C
cu cu
3 M
cr
cu co
PJ^ ^ 1
O
!N30d3d Nl AON3nO3Hd
0)
(-1
60
•H
-66-
-------
o
\
\
\
\ x°
\ ,£.
\
\
U
\ X
\
\
\
\
\
\
0 \
x \
II \
U V
X
\ \
\ \
X V
X X
X \
\
\
N \
X ,
\
X \
N \
\ \
X V
X X
\
** X
CN x \
II \ \
0 \ \
X \
^- N» \
\ x
N \
X .
^•x \ \
"v^ x- \
^ X \
^ ^ \ \
^v \ x
x^ \ . x
X \
^ x \
N» V
"•-.^ \ \
"" ^ * X \.
*^ x \ i
•» ^^ x.l
•v \ \
^^^ N \
rO
CM
0
-co
0
-CD
•••
T
o
r-
o
CM
o
-o
0
CO
o
"CD
O
Tfr
.0
CM
11)111
o o o o o o
CM O CO
•H -H
o to
CU
M
n) tu
4-J T3
n) c
T3 cd
C
^t M
F-I cu
J-i PO
3
O 0)
4J
O 4-1
cd
o to
I— 1 (1)
CX 0
c
t-i 0)
0) 1-1
4-1 1-4
4-1 3
cd o
0 0
en o
,-H
0)
M
M
•H
-67-
-------
ME -E
» »
00
1^
*
•o -o o
« o> •-
*•' > m
m C &
— a> cc
I 5
a O
O
(0 Q)
*- >
> **
<
O
O
CM
O
CO
0 o
CM y
en
QJ
o
a
QJ
M
S-l
a
CJ
a
o
1-1
o
14-1
en
a
o
•H
4-1
cfl
H
4J
C
QJ
a
c
o
o
CM
O
13
l-i
QJ
cn
Q)
4J
Ctf
rH
3
a
O o)
4-J
cn -H
C cn
o ••—'
•H
4-3 Q)
3 4-J
& -H
•H CO
!-i
4-1 r-H
W iH
•H Q)
T3 CO
cn
>, 3
O Di
C
a) QJ
3 ^
cr 4-)
QJ
M 4J
P4 Ct)
O
in
o
CO
o
CM
!N30H3d Nl
oo
•H
-68-
-------
\
\
\
\ °
\ X
\ ,N
\ »
0
\ X
\
\
\
\
\
\
0 \
x \
II \
O \
x x
N \
\ \
\ \
\ X
\ \
\ \
\
\ \
N\
v \
N \
\ \
\ .
\ \
N
x \ x
m \
IN \ \
II \ \
O \ v
\
X \ \
^ \ \
^ \
^ \ V
\ V
^ N \
^ \ N
^ \ \
^ \ \
X \ •
\ • \
\ \ .
"^ x \
^ • \
^ V \
^ \ . . x
x \ ;
^ V 1
*•• x \
0
rO
CM
0
-co
T-
0
-CD
i~
0
-^p
O
-CM
O
-0
0
CO
.0
(0
.0
vf
>l
o
^v. • \ > -jr.
• ^ ^ M .\* • x . *. CM
^ " *N \ '
• • . >V " • \ • r *•
: •. • ^^« \ \ .
^ \ »
^ \ \
•x. \
^i
666666
CM O CO CO ^ CM
T~ T~
->s
O)
3.
(/)
C
o
*^
03
^B
oncenl
O
•o
0)
>
o>
(A
^
0
o
X
^
o
MH
co
55
o
•H
4-1
cfl
s-i
4-1
C
OJ
0
a
o
o
CM
O
CO
Td
0)
>
M
CL)
CO
,£>
o
13
a
n)
73
0)
4J
Cfl
i-i
O ^
rH O
cfl rH
0 O
MH a)
O 4J
•H
CO CO
4-1 ^
G
•H 01
O 4J
CX -rl
Cfl
4-1 rH
Cfl rH
•n a)
co
>> CO
rH 3
«. ,^
H PS
O 0)
^2 &
4-1
MH
0 -tJ
03
O Cfl
rH OJ
a, o
rJ
M S
OJ P
4-1 M
4-1 3
ca a
o o
CO O
ri
o-
OJ
(euj/67/)
60
-69-
-------
t
Q
Cd
^>
K
Cd
8
O
1
0 Cd
a H
t. O Cfl S_
•P -H rH CD
C -P 3 CO
CD Cd O £>
O t. rH 'O
C 0)
O V
o ^
c
CM O
O -H
CO -P --N
CD cflro
hfl-0 f- S
t, > C bO
CD S-, CD 3.
> CD 0 •^
< CO C
X) O
0 0
CM
O C
CO O
•H
bO CD COCO
cfl -P t, S
t. CO -P \
Q) rH C bO
> 3 CD 3.
^3 ^~* t*~ ^*~ OO t"*~ vO f^
• •••*• » •
^rt- J^-T- CMO ovo CM^C m^*- c— «- CMO
LTi»— OCM OCM POv- CMCM CM1 — LTi«— CTiO
v»^ r— v_x t"~ -^ T— *-^ lfc-x ^" ^~
*— ^
rH
rH
OOOOOO»- ti
OOOOOOO CD
^
O
•
CQ
-------
100-
80-
-------
TABLE 14. CALCULATED AND OBSERVED AVERAGE S02 CONCENTRATIONS (yg/m3) AT
THE SEVEN S02 MONITORING SITES ARRANGED IN ORDER OF DECREASING VALUE
Site
number
004
003
005
002
008
010
009
Distance from
stack (km)
4.4
5.9
7.3
9.9
8.3
15.5
14.6
Calculated
value
74.4
56.8
46. 1
35.2
28.2
15.2
11.4
Observed
value
79.8
47. 1
37.3
35.2
35.0
19.3
15.4
Although the highest calculated concentrations did not correspond to the
highest measured concentrations on an hour-by-hour basis, the Gaussian model
is indeed capable of predicting the highest concentrations. Ragland (1976)
has developed analytical equations for the prediction of worst-case
concentration for a plume. The worst-case concentrations for a trapping plume
are twice as large as those for a coning plume. For distances beyond the
position of the maximum concentration the ratio between trapping and coning
may be larger than 2.
By means of Ragland's (1976) equations the highest possible concentration
at each stability class was calculated. Table 15 shows the worst-case
concentrations for coning and trapping plumes and the highest calculated and
observed concentrations for each stability class. When the worst observed
concentration is compared with the worst concentration for a trapping plume,
it can be seen that the model is capable of predicting the worst-case
concentrations for most of the stability classes.
TABLE 15, WORST-CASE OR HIGHEST S02 CONCENTRATIONS
(pg/m3) AT THE COLUMBIA GENERATING STATION--1976
Stability
class
A
AB
B
BC
C
CD
D
Coning
plume
282
211
139
124
109
69
28
Trapping
plume'
564
422
278
248
218
138
56
Calculated
191
245
134
124
252
152
53
Observed
247
243
203
178
178
102
157
-72-
-------
MOBILE MEASUREMENTS OF AIR POLLUTANTS DOWNWIND OF THE STACK
Because of the transient nature of the plume, the use of portable
monitoring equipment is important in establishing the highest ground-level
concentrations and provides a means of getting more data directly under the
plume. Plume traverses yield the concentration as well as the width of the
plume. These data were used to supplement the fixed-site data for the purpose
of model verification.
A Meloy flame photometric S02 analyzer and an Analytical Instrument
Development Corp. chemiluminescent ozone meter were operated from an
automobile. Once the plume was located, the plume was traversed along
available roads at several distances downwind. Each traverse was run at
constant vehicle speed until the S02 concentration could no longer be detected
above ambient levels. Since the ozone levels dropped to zero within the
plume, this was a sensitive way to track the plume. The S02 analyzer was
calibrated weekly with a Metronix permeation tube system. The ozone analyzer
was calibrated regularly at the Wisconsin State Hygiene Laboratory with a
standard source.
The ground-level concentrations downwind of the stack were highly
transient. With strong winds the plume is broken up into blobs. With
unstable conditions the plume touches down at varying spots and frequently the
wind veers.
Data for which we have good strip-chart recordings of S02 concentration
are summarized in Table 16. The maximum concentrations based on a 1-min
average are all less than 300 yg/m^. The mobile monitoring data generally
confirm the predictions of the Gaussian plume model.
PREDICTED ANNUAL CONCENTRATIONS OF S02, NITROGEN OXIDES, AND PARTICULATE
MATTER
In spite of all the scatter in the 492 hourly data points, the annual
average predicted by the model is very close to the annual observed value.
Not only are the overall averages close, but so are the annual averages for
each of the seven sites, as borne out by the correlation coefficient of 0.954,
based on the seven pairs of averages. Since the agreement between model
values and observed values was good, the model, including the sector
averaging, was used to predict the spatial distribution of the annual averages
of S02, nitrogen oxides, and particulate matter. The emission equations used
were those previously given in this section. The plume calculations were
performed for all 5,929 data hours of the year at each point of a rectanglar
grid and were averaged over the year. The results are given in Figures 45-4?.
The black dot in the middle of the isopleth represents the generating station
stack, and the seven x's show the locations of the S02 monitoring sites. The
drawings cover an area of 64 km2 around the generating station. As can be
seen from these figures, the annual increase of S02 concentrations was
1-3 Pg/m3 within 10-16 km of the generating station. About the same annual
incremental increase is noted in Table 7 between the pre-operational
monitoring data and the 1976 data. Predicted annual increases near the
-73-
-------
30
(0
i
I
CO
CD
20
10
d>
O
c
£ -10
CO
b
-20
-30
-30 -20 -10 0 10 20
Distance in Miles - South
30
Figure 45. Calculated 1976 average concentrations of S02 (ug/m3) near the
Columbia Generating Station.
• indicates Columbia Generating Station
x indicates monitoring site.
-74-
-------
30
20
CO
i «
I
CO
® 0
o
c
co
-«—•
CO
b
-10
-20
30l
-30
-20 -10 0 10 20
Distance in Miles - South
30
o
Figure 46. Calculated 1976 average concentrations of NCv, (yg/m ) near the
Columbia fienerafine- Sfal-ion.
— -— — - — tj ~ - - —
Columbia Generating Station.
• indicates Columbia Generating Station
x indicates monitoring site.
-75-
-------
30
CO
CD
I
CO
0
O
C
CO
*-•
CO
b
20-
10 -
-10
-20
-30
-30 -20 -10 0 10 20
Distance in Miles - South
30
Figure 47. Calculated 1976 average concentrations of particulate matter
(yg/m ) near the Columbia Generating Station.
• indicates Columbia Generating Station
x indicates monitoring site.
-76-
-------
TABLE 16. SUMMARY OF S02 MOBILE MONITORING DATA
NEAR THE COLUMBIA GENERATING STATION
Date
(1976)
6/25
6/25
6/25
6/25
6/25
8/2
8/5
8/5
8/5
8/11
8/11
8/11
8/11
8/11
8/21
8/21
9/28
Time
1340
1350
1440
1550
1530
1045
1040
1130
1100
1300
1320
1330
1400
1410
1100
1105
1300
Cloud
cover
10/10
10/10
10/10
10/10
10/10
0/10
8/10
10/10
8/10
4/10
4/10
4/10
4/10
4/10
3/10
3/10
0/10
Wind
direction
152
152
152
152
152
0
355
355
355
140
140
140
140
140
130
130
0
Distance
downwind
(km)
10.9
8.8
7.6
6.2
6.2
3.9
4.0
3-12
4.0
5.0
5.0
5.0
7.7
7.7
5.1
5.4
5.5
Vehicle
speed
(mph)
30
40
40
40
40
30
25
40
25
0
20
25
20
20
30
30
30
Plume
width
(km)
_— —
3.8
__-
6.8
3.5
2. 1
__-
2.5
1.0
1. 1
0.70
0.75
0.70
2.9
1.3
Maximum S02
concentration
(ug/m3)
170
78
120
73
74
288
113
65
131
170
144
79
100
136
141
63
107
generating station are 0.75 yg/m3 for nitrogen oxides and 1 pg/m3 for
participate matter.
-77-
-------
SECTION 4
DRY DEPOSITION OF SULFUR DIOHDE FROM THE COLUMBIA PLUME
Gaseous and particulate air pollutants emitted into the ambient air by a
large coal-fired electric generating station are removed by dry deposition,
precipitation scavenging, and chemical transformation. In the case of
particulates gravitational settling is not significant because the large
particles are removed by emission-control equipment, and the small particles
do not grow large enough to settle out. For most emissions dry deposition is
the principal removal mechanism from the ambient air. This section concerns
dry deposition of sulfur dioxide; the same principles apply to other
pollutants including small particles or aerosols.
Dry deposition of air pollutants is caused by impaction of the gaseous
molecules or the particles with the surface, which may be vegetation, soil,
water, or snow, for example. The rate at which pollutants are removed by
impaction depends on the pollutant type, surface material, surface roughness,
wind speed, and atmospheric turbulence. The deposition rate is important to
determine because it represents an input to the ecosystem and can be an
important factor in estimating the ambient air concentration further downwind.
The dry deposition flux of a particular pollutant is assumed to be
linearly proportional to the ground-level ambient air concentration of that
pollutant for a given surface and set of meteorological conditions. The
proportionality constant is referred to as the deposition velocity.
Deposition velocities have been measured in the field and in the laboratory
for a variety of situations to establish the range of likely values. Sulfur
dioxide has one of the highest deposition velocities of any air pollutant and
is an order of magnitude greater than small particulates.
Laboratory studies of dry deposition are useful for determining the
influence of various surface types, but cannot simulate representative
impaction conditions. Field studies, on the other hand, must be done with
very low ambient background concentrations of pollutant and have usually
required averaging times of hours. Representative recent dry deposition
studies may be found in Engelmann and Schmel 1976.
No measurements of S02 dry deposition have been reported directly in the
plume. Conceivably, transient deposition rates associated with a single plume
could be significantly larger than steady-state deposition associated with
more widespread background pollutant concentrations. Hence the objective of
this study was to measure the dry deposition flux of S02 directly in the plume
-78-
-------
with an averaging time of minutes. These results would then be more
appropriate to plume modeling work.
At least two methods are available for determining the dry deposition rate
in the field: the eddy correlation method and the gradient transfer method.
The gradient transfer method was chosen for this study because it is more
closely related to the modeling approach used for the plume dispersion and
deposition predictions.
During 1976 and early 1977 field experiments to determine the deposition
velocity of sulfur dioxide were conducted near the Columbia Generating
Station. The study area consisted primarily of flat agricultural fields with
some wetlands and woods. The area to the southwest of the stack just across
the Wisconsin River is the eastern terminus of the Baraboo Bluffs, which are
rolling 500-ft hills.
THEORY OF GRADIENT-TRANSFER METHOD
When no deposition occurs the concentration near the surface is
essentially constant with height near the ground when dealing with elevated
point sources. However, when removal at the surface occurs, a concentration
gradient with height is established which depends on the removal rate and the
atmospheric turbulence. By simultaneously measuring the concentration, wind,
and temperature near the ground, the deposition flux can be deduced. The
chief limitation of the method is the need for uniform terrain. Thus, it is
not effective near hedges or small woods, but is ideally suited for use over
extensive agricultural fields or marshes. The theory of the gradient-transfer
method may be found in Businger (1971) and Moneith (1971) and is summarized
below.
From Pick's law of diffusion applied to turbulent flow the deposition flux
F is given by
F(0) = K (*C) ,
z az z = o do)
where Kz is the vertical eddy diffusivity. If the deposition velocity concept
is used ,
F(0) = vdC(0) (17)
Combining equations (16) and (17) we have a convenient method for
determining the deposition velocity
z=0
Hence the concentration and concentration gradient near the ground must be
measured. The eddy diffusivity cannot be measured directly, but is determined
from the wind-speed gradient and temperature gradient. The assumption must be
made that the eddy diffusivity for momentum is equal to the eddy diffusivity
for mass according to Reynolds analogy. The method for determining the eddy
diffusivity is described below.
-79-
-------
The eddy diffusivity for momentum is defined in relationship to the shear
stress T by
T = P K -r^- .
M oz
(19)
If we introduce the friction velocity u» =/TO/P , where TO is the shear
stress at the surface, and use the fact that the shear stress is constant near
the surface, it follows that
2
u*
TT _ - _. _ /
For neutral atmospheric conditions the wind speed follows a logarithmic
profile,
u = 2.5u^ ln (^=-) , (21)
where d is the displacement height and zo is the surface roughness. The
friction velocity, u#, may be determined by a curve fit of the measured wind
data. The velocity gradient in Eq. (20) may also be determined by a curve fit
of the data. When Eq. (21) is substituted in Eq. (20),
KM = 0.4 u»(z-d) (22)
If KM = Kz, the deposition velocity may be determined from Eq. (18).
For non-neutral atmospheric conditions the wind-speed profile is more
complex than Eq. (21), and the friction velocity, u#, will take on somewhat
different values. However, in practice we were only able to locate the plume
on the ground for a 15- to 20-min period during near-neutral conditions. With
unstable conditions the plume was shifting too rapidly, and for stable
conditions the ground-level concentrations were too low. Hence, further
discussion of non-neutral meteorological parameters is not needed here.
The deposition process may be split into an aerodynamic component and a
surface component. The aerodynamic component is related to the turbulent
mixing near the ground; the surface component depends on how readily the
pollutant species is absorbed. To distinguish between these two processes, it
is convenient to introduce a transport resistance, r, as
r = 1/vd (23)
and note that r is the sum of the aerodynamic resistance, ra, and the surface
resistance, rs:
r = ra + rs (24)
From Eq. (17) it follows that
r _ C(0)
L ~~ —. /~ rt~\~ •
-80-
-------
By analogy with turbulent transport of momentum (Chamber.lain 1966) it may be
postulated that:
r" " ' " ^ ' (26)
If we equate the transport of momentum and the transport of mass in the
turbulent boundary layer, then:
rM = ra (27)
In this case the difference between the measured total resistance and the
aerodynamic resistance can be interpreted as the surface resistance. Some
people have attempted to account for an extra resistance arising from the
difference between mass and momentum transport, but this was not considered
necessary for our near-neutral conditions.
EXPERIMENTAL TECHNIQUE
The objective was to take deposition measurements in the plume on the
ground. The procedure was to locate the plume by means of the sulfur dioxide
analyzer and ozone analyzer, which were operated from an automobile. When in
the plume the S02 would increase and the 03 decrease (because of NO scavenging
the 63 to form NC>2). The area was traversed until the plume position was
established and shown to be near the maximum ground-level position, or at
least greater than 30 yg/m3. Also, a location with a large uniform surface
canopy had to be chosen. Since time was not available to request permission
to enter the land, an area where trespassing was not a serious problem had to
be selected.
Because of the transient nature of the plume and the desireability to set
up in many types of terrain, the equipment needed to have self-contained power
and be portable, light, compact, and easy to assemble. The following
equipment was used:
(1) A Meloy Inc. S02 analyzer Model SA165 with a sample flow rate of
200ml/min.
(2) fiimco cup anemometers with a built-in light system to count the number
of revolutions of each anemometer.
(3) Esterline Angus stripchart recorder (portable).
(4) Iron-constantan thermocouples (radiation shielded) ice bath, stepping
switch, and Wheatstone bridge.
(5) Bendix air sampling pump, Model BDX-44.
(6) Teflon air sampling bags fitted with teflon tubing and mounted in a
plexiglas chamber.
(7) An aluminum mast, 2.2-m high, with five brackets each of which could
position a cup anemometer, teflon tube, and thermocouple.
-81-
-------
At each site, measurements of wind speed, temperature, and S02
concentration were made simultaneously at heights of 0.125, 0.25, 0.50, 1.0,
and 2.0m above the vegetation canopy. The mast had the capability of small
height adjustments, but in the case of tall canopies only three measuring
heights were used: 0.50, 1.0, and 2.0 m. Care was taken in setting up the
instruments not to disturb the upwind and downwind canopy.
Sulfur dioxide profiles were determined by sampling at the five heights
simultaneously through small teflon tubes. The ambient air was drawn through
the tubes into teflon bags by pumping a slight vacuum in the plexiglas box.
After a sampling period of 15 min the ends of the sampling tubes were sealed,
and then each bag was connected to the 302 analyzer. The S02 analyzer and the
bags were calibrated regularly with a Metronics Dynocalibrator which used a
S02 permeation tube.
DATA COLLECTION AND ANALYSIS
In eight cases satisfactory data were obtained. Approximately 35 trips
were made to the site to collect data over a period of 1 yr, but locating the
plume for any length of time proved very difficult. With intense summer sun,
which results in relatively high instantaneous ground-level concentrations,
the plume fluctuations were too large to allow sufficient sampling time.
Cloudy conditions or time when the sun was not too high provided the best
opportunity to obtain data, although even then the wind might veer away from
the selected site before the test could be completed. Of course, if the
temperature (including chill factor) was too low, the tests were too difficult
to run.
The data for the eight completed cases are given in Table 17. The time,
site features (including height of the vegetation canopy), cloud cover, wind
speed, temperature, and concentration data are recorded for each test. The
wind and concentration data are plotted versus the log of the height above the
aerodynamic displacement height. For some of the tests one or more measuring
heights was below the canopy height; these data are noted with an asterisk and
are not plotted.
To analyze the data, the displacement height, d, is first determined by
relating the measured winds at three heights with the log profile relationship
(Eq. (21)):
u, —u
T5(z1-d)-in(z.-d) *
3 "uv"l "' "v 3 "' (28)
Once d is determined, the wind, concentration, and temperature are plotted
versus n(z-d). The next step is to determine the friction velocity, u», and
the surface roughness, zo, from the plots. By rewriting Eq. (21) in the form
u = 2.5uA/n(z-d) -2.5u^ ln QZ , (29)
it is apparent from the plots that
u* = 2.5/slope of velocity (30)
-82-
-------
TABLE 17. DATA FROM EIGHT FIELD TESTS IN WHICH S02 DEPOSITION
WAS MEASURED NEAR THE COLUMBIA GENERATING STATION
Test 1
Site: Pasture, County P 5 G
Time: 5:00 p.m.
Date: 8-11-76
Canopy Height: 16 cm
Richardson Number at 1 m: -.053
Location
1
2
3
4
5
Height (M)
2.00
1.00
0.50
0.25
0.125
Wind Speed (m/s)
2.50
2.15
1.58
1.28
0.36
Temperature (°C)
32.3
32.8
33.3
34.0
36.4
Concentration
(vg/m3)
49
47
50
43
31
0.8
0.0
-1.0
I
^N
C
-2.0
-3.0
-4.0
O WIND SPEED
. D CONCENTRATION
i
30
SLOPE = 2.07
1.0 2.0 3.0
WIND SPEED (m/s)
i i i
40 50 _ 60
CONCENTRATION (ug/m3)
-83-
-------
Table 17 (continued)
Test 2
Site: Marsh, County G
Time: 1:20 p.m.
Date: 9-22-76
Observations: soil-wet, slightly unstable, 1/10 cloud cover
Canopy height = 81 cm
Richardson Number at 1 m: -.035
Location
1
2
3
4*
5*
Heigth (m)
2.31
1.31
0.81
0.56
0.43
Wind Speed (m/s)
2.41
1.75
0.90
0.74
0.36
Temperature (°C)
20.7
23.6
25.8
25.0
24.1
Concentration
(vg/m3)
87
80
69
60
51
0.8
0.0
-1.0
•o
I
-2.0
-3.0
SLOPE = .103
O WIND SPEED
D CONCENTRATION
SLOPE =1.25
60
1.0 2.0
WIND SPEED (m/s)
i i
70 80
CONCENTRATION (ug/m3)
3.0
i
90
-84-
-------
TABLE 17 (continued)
Test 5
Site: Cut alfalfa, Cunnings Road
Time: 2:00 p.m.
Date: 9-29-76
Observations: Very dry sandy soil, patchy alfalfa, 0/10 cloud cover, windy.
Canopy height = 15.5 cm
Richardson Number at 1 m: -.058
Location
1
2
3
4
5
Height (m)
2.00
1.00
0.50
0.25
0.125
Wind Speed (m/s)
3.87
3.33
2.80
2.18
1.01
Temperature (°C)
17.1
17.7
18.5
19.9
21.2
Concentration
(vg/m3)
149
147
142
139
127
1.0
0.0
•o -1.0
i
-2.0
-3.0
O WIND SPEED
D CONCENTRATION
SLOPE =1.51
SLOPE =0.23
0 1.0 2.0 3.0 4.0 5.0 6.0
WIND SPEED (m/s)
120 130 140 150
CONCENTRATION (ug/m3)
-85-
-------
TABLE 17 (continued)
Test 4
Site: Tall prairie
Time: 2:25 p.m.
Date: 10-7-76
Observations: Grass damp, rain day before, 3/10 cloud cover.
Canopy height: 66 cm
Richardson No. at 1 m: -.19
Location
1
2
3*
4*
5*
Height (m)
2.00
1.00
0.50
0.25
0.125
Wind Speed (m/s)
1.72
1.20
1.15
0.66
0.22
Temperature (°C)
12.2
13.6
14.8
16.8
19.3
Concentration
(vg/m3)
50
37
36
39
37
0.6
0.0
-1.0
i
N
-2.0
-3.0
i
30
SLOPE = 0.076
O WIND SPEED
O CONCENTRATION
"SLOPE = 1.89
1.0 2.0 3.0
WIND SPEED (m/s)
i i i
40 50 60
CONCENTRATION (ug/m3)
-86-
-------
TABLE 17 (continued)
Test 5
Site: Pasture
Time: 4:30 p.m.
Date: 10-7-76
Observations: Moist soil, 3/10 cloud cover.
Canopy Height: 6 cm
Richardson Number at 1 m: 0.0 (assumed)
Loaation
1
2
3
4
5
Height (m)
2.00
1.00
0.50
0.25
0.125
Wind Speed (m/s)
2.44
2.02
1.74
1.43
0.92
Temperature (°C)
---
—
---
Concen tra tion
(vg/m3)
65
64
60
43
57
06
0.0
-1.0
•o
i
N
-2.0
-3.0
SLOPE = 0.35
O WIND SPEED
D CONCENTRATION
0 1.0 2.0 3.0
WIND SPEED (m/s)
i i i i
40 50 60 70
CONCENTRATION (ug/m3)
-87-
-------
TABLE 17 (continued)
Test 6
Site: Marsh
Time: 3:20 p.m.
Date: 12-10-76
Observations: Soil-dry, slightly unstable atmosphere, 3/10 cloud cover.
Canopy height: 80 cm
Richardson Number at 1 m: -.016
Location
1
2
3
4
5
Height (m)
2.50
1.50
1.25
1.00
.75
Wind Speed (m/s)
4.39
3.13
2.82
2.22
1.05
Temperature (°C)
6.4
6.7
7.1
8.2
9.6
Concentration
(vg/m3)
58
52
56
54
52
0.8 -
0.0
Jj-1.0
-2.0
-3.0
i
42
SLOPE=.634
SLOPE =.625
SLOPE = .347
O WIND SPEED
A WIND SPEED-CORRECTED
D CONCENTRATION
1.0 2.0 3.0 4.0 5.0 6.0
WIND SPEED (m/s)
i i i
50 60 70
CONCENTRATION (ug/m3)
-88-
-------
TABLE 17 (continued)
Test 7
Site: Snow covered field
Time: 12:45 p.m.
Date: 2-15-77
Observations: Old-melting snow, 0/10 cloud cover.
Canopy height = 0.0 (m)
Richardson Number at 1 m:
Location
1
2
3
4
5
Height (m)
2.00
1.00
0.50
0.25
0.125
Wind Speed (m/s)
4.72
4.21
3.67
2.94
2.54
Temperature ( °C)
1.0
---
—
---
1.0
Concentration
(vg/m3)
130
126
123
118
115
1.0
0.0
•o
N
-1.0
-20
-3.0
i i r
SLOPE=.I87
SLOPE =1.27
OWIND SPEED
D CONCENTRATION
0 1.0 2.0 3,0 4.0 50 6.0 70 80
WIND SPEED (m/s)
i i i i i
100 120 140 160 180
CONCENTRATION (ug/m3)
-89-
-------
TABLE 17 (continued)
Test 8
Site: Wet land prairie
Time: 1:00 p.m.
Date: 3-8-76
Observations: Soil wet, 0/10 cloud cover, gusty wind.
Canopy height =
Richardson Number at 1m: -.011
Location
I
2
3
4
5*
Height (m)
2.00
1.50
1.00
0.75
0.50
Wind Speed (m/s)
3.85
3.50
2.76
1.96
1.75
Temperature (°C)
16.8
17.4
18.0
18.0
Concentration
63
61
61
59
53
08
0.0
T3 -1.0
I
N
-2.0
-3.0
40
O WIND SPEED
D CONCENTRATION
SLOPE = 1.15
SLOPE = .475
1.0 2.0 3.0 4.0 5.0 60
WIND SPEED (m/s)
i i i i i i
45 50 55 60 65 70
CONCENTRATION (ug/m3)
-90-
-------
and
^n z0 = y intercept (31)
The information needed to calculate the eddy diffusivity, K^, is now available
for Eq. (22). However, at this point we must choose a reference height for
% and V(j since experimentally z = 0 cannot be used. A 1-m reference height
was chosen so that:
KM(1) = 0.4u»(1-d) (32)
The concentration gradient at the reference height is determined from the
slope of the concentration plot as:
= _ _ _
dz (slope of concentration plot)(l-d)
Finally, the deposition velocity at the reference height of 1 m is
determined from Eq. (18) as:
0.4 u^
d C(l)(slope of concentration plot)
The temperature was measured at each height to indicate the stability of
the atmosphere, wh?ch may be characterized by the Richardson Number,
Ri
JL §
T (3u/8z)Z (35)
where T is the adiabatic lapse rate. For a neutral condition Ri = 0, for
unstable conditions Ri < 0, and for stable conditions Ri > 0. The above
theory can be modified for non-neutral conditions. For example,
KM = 0.4 u^Cz-dXl , (35)
where a= 2.5 in stable conditions and a = 9 in unstable conditions. For
our experiments we did not feel that these corrections for atmospheric
stability were justified.
RESULTS AND DISCUSSION OF DEPOSITION MEASUREMENTS
The results of the field test data are summarized in Table 18. The
deposition velocities range from 0.21 to 1.8 cm/sec. The two tests in pasture
showed similar results of 0.35 and 0.32 cm/ sec. The two tests over marsh land
gave results of 0.75 cm/sec, although one test was done in fall and one in
early winter. The cut alfalfa site gave a value of 0.30 cm/ sec, which is
reasonable in view of the dry conditions. The tall prairie site (run 4),
which was still damp after an October rain, showed the highest deposition
velocity of 1.8 cm/sec. However, another prairie site (run .8), measured in
March when the soil was wet but the vegetation dry, registered the lowest
value of 0.21 cm/ sec. One test was completed over old wet snow, and a
deposition velocity of 0.55 cm/ sec was determined.
-91-
-------
TABLE 18. SUMMARY OF S02 DEPOSITION MEASUREMENTS—REDUCED DATA
C(1) z0 d u* u(1) Km(1) u. .
Run (pg/nP) ( m ) Ri(1) (—m/sec—) (n^/sec) (cm/sec) Canopy
1
2
3
4
5
6
7
8
47.
80.
146.
36.
64.
53.
125.
60.
0
0
7
7
2
7
8
8
0.0076
0.086
0.006
0.060
0.008
0. 130
0.0047
0.016
0. 10
0.54
0,098
0.42
0.036
0.50
0.00
0.60
-0.
-0.
-0.
-0.
0.
-0.
0.
-0.
053
040
058
19
0
070
0
011
0.20
0.44
0.27
0.22
0.18
0.66
0.32
0.37
2.15
1.75
3.33
1.19
2.02
2.22
4.21
2.76
0.074
0.062
0.100
0.050
0.071
0. 140
0.130
0.061
0.35
0.75
0.30
1.8
0.32
0.75
0.55
0.21
pasture
marsh
cut alfalfa
tall prairie
pasture
marsh
wet snow
prairie
The resistance to deposition for each of the tests is shown in Table 19.
The surface resistance was always significantly larger than the aerodynamic
resistance, indicating that the surface sink is more of a limiting factor than
the vertical transport by wind. Aerodynamic resistance was comparable to
surface resistance only for tall prairie. Hence, from a plume modeling point
of view, accurate knowledge of the surface characteristics is generally more
important than the friction velocity when calculating the dry deposition flux.
TABLE 19. SUMMARY OF TOTAL-DEPOSITION-RESISTANCE, AERODYNAMIC-
RESISTANCE AND SURFACE-RESISTANCE DATA FOR SULFUR DIOXIDE
Test r (sec/cm) ra rg Canopy
1
2
3
4
5
6
7
8
2.8
1.3
3.3
0.55
3.1
1.3
1.8
4.7
0.5
0. 1
0.5
0.25
0.6
0.05
0.4
0.2
2.3
1.2
2.8
0.30
2.5
1.3
1.4
4.5
pasture
marsh
alfalfa
tall prairie
pasture
marsh
wet snow
prairie
In general, test results for the deposition velocity were similar to other
values (Chamberlain 1966), which were obtained under steady-state ambient
conditions as opposed to our tests in the plume. No evidence was found to
suggest that transient conditions result in higher deposition rates.
The results reported here should be regarded as tentative because of the
limited amount of data obtained and the impossibility of repeating the
experiments under the same conditions. As mentioned, these difficulties were
caused by the rapid shifting of the plume.
-92-
-------
SECTION 5
CALCULATION OF DRY DEPOSITION OF SULFUR DIOXIDE FROM THE COLUMBIA PLUME
In this section the dry deposition of S02 from the plume is calculated by
means of ambient air monitoring data and deposition velocity. Deposition due
to the plume is distinguished from deposition due to background S02- The S02
deposition is an indicator of sulfate loading to the soil or water, if S02 may
be converted to sulfate ions at the surface. Other sulfate loading can occur
from wet deposition of sulfates and S02- The S02 dry deposition flux was
calculated at the monitoring sites (Figure 5). The hours when the plume from
the station were striking a monitoring site were determined from wind
direction and the increase in concentration at the site in the wind sector as
compared to other sites at that hour. This incremental concentration value
was multiplied by deposition velocity and summed for each hour of plume strike
during the year. A deposition velocity of 1 cm/sec was used during
April-November, and a value of 0.3 cm/sec was used during of December-March.
Monitoring sites 3 and 4 recorded the most plume strikes during the year
and hence had the highest deposition flux (Table 20). When corrected for
missing data, the deposition flux was 0.508 and 0.437 kg/ha/yr at sites 3 and
4. The other sites had lower deposition because they were farther away or in
a less frequent wind sector. With the same method the S02 dry deposition flux
due to background concentrations at the monitoring sites is estimated to be 15
kg/ha/yr. Hence, the plume from the generating station contributes 3% of the
regional dry deposition of S02 annually.
TABLE 20. DEPOSITION OF S02 FROM THE PLUME AT THE MONITORING SITES
Monitoring Hours Hours of S02 deposition
site sampled plume strike (kg/ha/yr)
2 2,329 21 0.158
3 7,952 194 0.508
4 7,952 198 0.437
5 6,869 127 0.115
8 5,623 61 0.067
9 5,623 42 0.037
10 5,623 98 0.090
-93-
-------
REFERENCES
Bacci, P., G. Elisei, and A. Longhetto. Lidar Measurement of Plume Rise and
Dispersion at Ostiglia Power Station. Atmos. Environ., 8:1177-1186, 1974.
Barber, F.R., and A. Martin. Further Measurements Around Modern Power
Stations—I-III. Atmos. Environ., 7:17-37, 1973.
Bowers, J.F., Jr., and M.E. Cramer. West Virginia Power Plant Evaluation.
EPA-903/9-75-002, U.S. Environmental Protection Agency, Philadelphia,
Pennsylvania, 1975. 57 pp.
Briggs, G.A. Plume Rise: A Recent Critical Review. Nuclear Safety,
12(1): 15-54, 1971.
Briggs, G.A. Chimney Plumes in Neutral and Stable Surroundings. Atmos.
Environ., 6:507-510, 1972.
Businger, J.A. Flux-Profile Relationships in the Atmospheric Surface Layer,
J. Atmos. Sci., 28:181-187, 1971.
Casanady, G.T. Turbulent Diffusion in the Environment. D. Reidel Publishing
Co., Boston, Massachusetts, 1973. 238 pp.
Chamberlain, A.C. Transport of Gases to and from Grass and Grass-like
Surfaces. Proc. Roy. Soc., A 290: 236-65, 1966.
Conference on atmosphere-suface exchange of particulate and gaseous
pollutants, Engelmann, R.J., and Schmel, G.H., ERDA Symposium Series 38
(CONF 740921), 1976. 224pp.
Giffbrd, F.A. Turbulent Diffusion-Typing Schemes: A Review. Nuclear Safety,
17(D:25-43, 1976.
Hino, M. Maximum Ground Level Concentration and Sampling Time. Atmos.
Environ., 2:149-165, 1968.
Klug, W. Dispersion from Tall Stacks. EPA-600/4-75-006, U.S. Environmental
Protection Agency, Washington, D.C. 83 pp.
Lee, R.F., M.T. Mills, and R.W. Stern. Validation of a Single Source
Dispersion Model. In: Sixth NATO/CCMS International Technical Meeting on
Air Pollution Modeling, Washington, D.C., 1975. pp. 463-511.
-94-
-------
Mills, M.T. , and F.A. Record. Comprehensive Analysis of Time-Concentration
Relationships and Validation of a Single Source Disperison Model,
EPA-450/3-75-083, U.S. Environmental Protection Agency. Research Triangle
Park, North Carolina, 1975. 143 pp.
Mills, M.T. , and R.W. Stern. Model Validation and Time Concentration Analysis
of Three Power Plants. EPA-450/3-76-002, U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina, 1975. 161 pp.
Moneith, J.L. Principles of Environmental Physics. American Elsevier, New
York, New York, 1973. 403 pp.
Pasquill, F. Atmospheric Diffusion. Ellis Horwood Limited, Chichester,
England. 429 pp.
Ragland, K.W. Worst-Case Ambient Air Concentrations from Point Sources Using
the Gaussian Plume Model. Atmos. Environ., 10:371-374, 1976.
Sauter, G.D. A Generic Survey of Air Quality Simulation Models. Laurence
Livermore Laboratory, University of California, Livermore, California,
1975. 110 pp.
Sutton, 0.G. Micrometeorology. McGraw-Hill Book Company, Inc. New York, New
York, 1953. 333 pp.
Turner, B. Workbook of Atmospheric Dispersion Estimates. AP-26, U.S.
Environmental Protection Agency, Washington, D.C., 1970. 52 pp.
U.S. Atomic Energy Commission. Safety Guide: 23 Onsite Meteorological
Programs. U.S. Atomic Energy Commission, Washington, D.C., 1972. 102 pp.
Weidner, G. Topographical Influence on Surface Winds near Portage, Wisconsin.
M.S. Thesis, University of Wisconsin-Madison, Madison, Wisconsin, 1976.
212 p.
Wisconsin Power and Light Company. Stack Test Results. Wisconsin Power and
Light Company, Madison, Wisconsin, 1976.
-95-
-------
APPENDIX
Printout of Program GAUSPLM
3 FUW,SIZ GAUSPLM
1 DIMENSION S02(7,31,24),Km,ANG(/)
2 DIMENSION AL(9),AZ(9),bZ(9),CZ(9),DZ(P),AY(9),bY(9),CY(9),DY(9)
3 DIMENSION STABU5),WD(2)
a DIMENSION PANG(9J
15 DIMENSION GMwC24)
16 DIMENSION L(21),K(ll,2l),KOUNTK{ll),FRtO(21)
17 DATA AL/.4,.34/5,.29b,.2475,.2,.16t>,.13,l.,.098/
18 DATA AZ/.0004S2,.000226,.!156,.0579,.222,.111,.764,1.,.746/
19 DATA HZ/2.1,2.1,1.09,1.09,.911,.911,.836,I.,.5tt7/
20 DATA CZ/0.,.OS/9,0.,.111,0.,.392,0.,1.,0./
21 DATA DZ/1.,1.09,1.,.911,1.,.636,1.,1.,1./
22 DATA AY/.000452,.000226,.115t),.Ob79,.222,.111,1.096,1.,S.7/
23 DATA BY/2.1,2.1,1.09,1.09,.911,.91 1 , <5<4, 1 . , . 366/
24 DATA CY/0.,.05/9,0.,.Ill,0.,.94fl,0.,1.,0./
2b DA)A DY/1.,1.09,1.,.911,l.,.S4,l.,!.,!./
26 DATA STAB/1.,1.5,?.,3.,3.,1.5,2.,2.5,3.5,4.,£!.,3.,3.,'J.,4./
27 DATA HS/152.4/
26 DATA PANG/.6864,.6020,.51S4,.435
-------
S6
57
5B
60
61
62
63
64
65
66
67
6tt
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
S
N
0
U
n
R
26 F
I
]
]
1
I
I
510 >
515 F
505 (
111 [
I
)
(
>
J
I
3900 t
I
3950
I
1
3980 1
3990 I
(
3995
4000 (
1 I
5 1
1
I
6 1
1
7
198
201
202
203
204
205
SUM2H=0.
SUM24=0.
NMONTH=12
DO 3000 MONTH=1,NMONTH
READ(25,26) NDIM,NORP
FORMAT(I2,11)
IBOD=1
IBID=NURP
IF(NORP.GE.6) 1BUDS2
1F(NURP.GE.6) IBID=7
DO 505 IsIBOD,iB10
DO 510 J=1,NDIM
KEAD(25,515,END=111) (S02.( I, J,KTT) ,KTT=1,24)
FORMATC32X,12F4.0/32X,12F4.0)
CONTINUE
DO 4000 1=1,7
DO 4000 J=l,31
KANT=0
DO 4000 Kl=l,23
1F(SU2(I,J,K1).LT.10..UR.SU2(I,J,K1).G1.9000,
KP1=K1+1
1F(S02(I,J,KP1).GT.9000.) GO TU 3995
DIF = ABS(S02U,J,Kl)-S02(i,J,KPl))
IF(01F-2.) 3900,3900,3950
KANT=KANF+1
IF(KANT.tQ.l) K2=K1
IF(K1.EQ.23) GO 10 3950
GO TU 4000
1FCKANT.LT.4) GO TU 3990
) GU TO 3995
DO 3980 1D=K2,K3
302(1,J,IDJ=0.
CONTINUE
KANT=0
GO TU 4000
lF(KANr.UE.4) 1,0 TU 39bO
CONT1NUF
KEAD(15,b,tND=50u) IY,M,1D,(GMw(1),I=1,24)
FORMAT(3(I2),24(F3.0))
DO 1000 1H=1,24
KEADC?0,6,tND=50U) SK,C4,WO(1),WD(2),C32,C34,S«S
FOHMAT(12X,F5.2,20X,F5.1,5X,2(F5.0),3(F5.1))
IF(M.EQ.l) GO TO 198
KFAOC20,7)
FORMAF(IX)
IBOY=IFIXt(«Dll)-«DC2))/lttO.)
IF(IBOY) 201,205,203
IFU6UY.LE.-2J GO TO 202
hD(l)=«U(l)+360.
GO-TO 205
D1R=WD(1)
GO 10 30
IF(1BOY.GE.2) GU TO 204
WD(2)=VD(2)+360.
GO TO 205
DIR=WD(2)
GU 10 30
FUD=ABS(WD(1)-WD(2))
IF(FUD.GT.'I5.) GO TO 1000
-97-
-------
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
1/1
172
173
174
175
206
207
30
31
32
8005
8010
8015
530
525
535
520
5201
521
800
810
825
400
405
410
niR=(rtO(l)+rtD(2J)/2.
IKUIR-777.) 206,1000,1000
IF(DIH-360.) 30,30,207
DIR=DIR-360.
DO 200 J=lBOD,IbIu
IF(J.NE.LZAHL) GO TO 200
IF(J.GT.4J GO TU 31
ISITE=Jtl
GO FO 32
ISITE=J+3
= ABS(L)IR-180.-ANG(J))
IFUHETA1.GT.22.5.AND.THETA2.GT.22.5) GO TO 200
IF(S02(J,ID,IH).LT.10..0R.S02(J,IL»,1H).GT.9000.J
IF(THETA1-THETA2) 8005,8010,8010
THETA=DIR+180.-ANG(J)
GO TO 8015
THETA=DIR-180.-ANG(J)
S02bsS02(J,ID,lH)
S02(J,IU,iH)sO.
NAB=NORP-1
AVE=0.
S02MAX=0.
DO 520 1=1000, ItilO
IF (802(1, ID, IH) -9999. J 525,530,530
S02U,ID,IH)=0.
NA8=NAB-1
IFCS02C1,IO,IH).LT.10.)
AVE=AVE+S02(I,IO,IH)
IF(S02(I,10,IH)-S02MAX)
S02MAX = i>02d,lD,Ih)
CONIlNUt
IF(NAB.EQ.O) GO Tu 200
AVE=AVE/NAB
DO 5201 1=1, ?1
IF(AVE.GT.Ld)) K(9,I)=K(9,
CONTINUE
KOUNTR(9)=KOUNTK(9)tl
SUMlsSUMl+AVE
GO TO 199
SO?(I,IO,1H)=0.
520,535,535
S02bB=SU2b-AVt
IF(S02Bb-10.) 1000,1000,521
IF(ABS(C32),GI.50.) GO [0 800
IF(ABS(C34).GT.50.) GO 10 810
US=(AbS(C32)+ABS(C34))/2.
GO TO fl25
IF(A8S(C34).61.50.) GO TO 1000
US=ABS(C34)
GO TO 825
US=ABS(C32)
IF(US.LT.L) GO TO 1000
USI=2.237*Ui>
IF(GM«dH).t_T.lOO.) GO TO 1000
IF(GMW(IH).LT.J75.) GO TO 400
If-(GMHdHJ.LT.400.) GO 10 405
TCH=.5426*GMW(lH)tl.l5
GO TO 410
TCH=.3394*GMW(lH)t42. /4
GO TO 410
TCH=.5137*GMW(IH)+12.4
IF(SR.GT.4..0R.S«S.GT.50.) GO To 1000
-98-
-------
176
177
178
1 /9
180
181
182
IFUH.LT./.UR.IH.bT.
IFCSWS.UT.O.) 11=1
IF (SWS.GT.l .99) 11=2
IF(SWS.bT.2.99) 11=3
IFCSWS.bT.3.99) 11=4
IF(SWS.liT,6.) 11=5
JJ=10
Gu 10 139
163 IFCSR.GT..4J JJ = 5
IF(SR.GT..817J JJ=0
KK=IItJJ
186 SN=STAB(KK)
187 IFCSR.LT..015) SN = 4.
188 GU 10 141
189 139 SN = 5.
190« IFCSWS.GT.4. ) SN = 4.
1^1 141 ISTAB=IFIX(2*(SN-.5))
192 IF(C4.GT.40.) C4=20.
193 TAK=C4+d73.
19« ZM=1500.
195 C CMS IS THE SIACK FLOrt RATE IN CUbIC MEIERS PER SECUND
196 CMS=1 .7*GMw(IH)tl03.9
197 C TS IS THE STACK GAS EXIT TEMPERATUKE IN DEGREES FAHRENHEIT
198 C TA IS THE AMblENT AIR TEMPERATURE IN DEGREES FAHKENHEIT
199 C rHE FOLLOWING STEPS CHANGE TS AND TA Tu ABSOLUTE TEMPERATURE SCALE IN
200 C DEGREES KELVIN
201 TSK=.064*GMW( IH) t370.8
202 C UH IS THE GAS EXIT HEAT FLUX IN KCAL PER SECOND
203 QH=eq.88*CMS*( TSK-TAK J/1SK
204 IF(IST Ab.GT.4) GO TO 20
205 C OH IS THt PLUME RISE IN ME1EKS
206 C THE FOLLOWING EQUAHUN GIVES OH IN UNSIAbLE AND NEUTKAL ATMOSPHERES
207 DH = 2.47*CbRI (UH J * CHS* * . 6666 / ) /US
208 GO 10 2b
2u9 C STATEMENT ^0 GIVES DH IN SlAoLE ATMOSPHERES
210 £0 OH =
-------
243
244
245
246
247
248
249
250
251
252
253
251
255
256
257
258
259
260
261
262
263
261
265
266
267
268
269
270
271
272
273
271
275
276
277
278
279
260
281
282
283
281
285
286
287
288
289
290
291
292
293
291
295
296
297
298
299
300
THfc. FOLLOWING CONCENTRATIONS AkF IN MICROGR AMS/CUBIC MtTER
CCSU2=CC*QS02
CBAR=2.5066*SIGMAy*CCS02/X/PANG(ISTAB)
IFCCBAR.GT.CCS02) C6AR=CCS02
GO TO 302
301 CCS02 = 0.
CBAK=0.
GO TO 302
302 DO 304 I = U21
IF(CBAR.GT.LU)) K ( J, I )=K ( J, I )+l
IF(CBAR.GT.LU) ) K (6, 1 )=K (8, I ) f 1
IF(S02BB.GT.L(I)) K ( 1 0 , 1 ) =K ( 1 0, 1 ) 1 1
IF(CCS02.GT.L(I)) K ( 1 1 , 1 ) =K ( 11 , 1 ) +1
304 CONTINUE
KOUNTR(J)=KOUNTR(J)+1
KOUNTR(8)=KOUNTR(8)tl
KOUNTR(10)=KOUNIR(lom
KOUNTR(ll)=KOUNfR(ll)+l
SUM2=SUM2+SU2B8
SUM3=SUM3fC6AR
SUM4=SUM4tCCS02
SUM3P=SUM3P+CBAK*CBAR
SUM2P=SUM2P+S02dB*SD2B8
1993
200
1000
500
2550
2600
2650
3000
WRITE(6,303) IY,M,lL),lH,lSITE,SN, ! Ht T A , I) I R , US I , AV£, S02MAX , Sl)2b , CCS
C02rC8AR,S02BB
FORMATUX,5(I2,3X),F3.1,3X,F5.1,3X,a(h5.1,5X))
GO TO 200
MAC=MAC+1
AVE=0.
NAB=NORH
00 1999 I=IBOD,1HIO
IF(i>02(I»ID,lH)-9999.) 1 99 1 , 1 992 , 1 992
S02(I,ID,IH)=0.
NAB=NAB-1
IF(S02U,ID,IH).LT.10.) S02 ( I , I L», 1 H) =0 .
AVE=AVE+SU2(I,IU,1H)
CONTINUE
IF(NAB.hQ.O) GO Tu 200
AVE=AVE/NAB
SUM1=SUM1 +AVE
KOUNTR(9)=KOUNTK(9)+1
OU 1993 1=1,21
IF(AVE.GT.Ld)) K (9, U =K (9, I ) + 1
CONTINUE
CONTINUE
CONTINUE
GO TO 1
CALL CLUSE(15,0)
CALL CLUSE(cJO,0)
CALL CLUSE(25,0)
CALL 10 f PSP ( 1 5, *2bOO )
GO TO 2550
CALL IOTPSP l?0 , 42650 )
GO TO 2600
CALL 10TPSP125, i300U )
GO TO 2o50
CONTINUE
WRITE(-f-) l(K(l,J),J=l,2l),I=l,10)
00 3600 J=l,ll
-100-
-------
301 DO 3500 1=1,21
302 IF(KC)ONTR(JJ.EQ.O) bO TO 3510
303 FREU(I)=100.*FLOAT(K(J,i))/FLUAl UOUN[R(J) )
304 3500 CONTlNUt
305 GO TO 3540
306 3510 DO 3525 1=1,?l
307 FRFQ(1)=0.
308 3525 CONTINUE
309 3540 bRITE(6,3550) (FRtQ(I),1=1/21)
310 3550 FORMAT(1X,21(FS.1,1X))
311 3600 CONIINUt
312 Ay/El=SUMl/KOUNTR(9)
313 AVE2=SUM2/KOUNTKC10)
315
316
317
318
319 (
320
321
322
323
324
325 9999
326
327
328 olXQI
329 d)F IN
EOF..
J
I = 1 , 1 1 )
WKITEC-,-) AVLl,AvE2,AVL3,AVEt4, (KUDNTH ( i )
TOP=FLOAT(KUUNTK( 10))*SUM23-SUM3*SUM2
BUTTOM=(FLOATIKOUNTR(10))*SUM3P-SUM3*SDM$)*(FLOAT(KUUNTK(10)
RCOK=TOP/SQKTIHUTIOM)
I«KIIF'(-,-) KCUR
RtWiNU 15
REWIND 20
REWIND 25
CONTlNUt
SI OP
END
-101-
-------
TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO.
EPA-600/3-80-048
4. TITLE AND SUBTITLE
AIR POLLUTION STUDIES NEAR A COAL-FIRED POWER PLANT
5. REPORT DATE
May 1980 issuing date
6. PERFORMING ORGANIZATION CODE
Wisconsin Power Plant Impact Study
3. RECIPIENT'S ACCESSION NO.
7. AUTHOR(S)
Kenneth W. Ragland, Bradley D. Goodell, and Terry L.
Coughlin
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Department of Mechanical Engineering
University of Wisconsin-Madison
Madison, Wisconsin 53706
10. PROGRAM ELEMENT NO
1NE831
11. CONTRACT/GRANT NO.
Grant R803971
12. SPONSORING AGENCY NAME AND ADDRESS
Environmental Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Duluth, Minnesota 55804
13. TYPE OF REPORT AND PERIOD COVERED
Final; 7/75-7/78
14. SPONSORING AGENCY CODE
EPA/600/03
15. SUPPLEMENTARY NOTES
16 ABSTRACT
Concentrations of dry deposition of sulfur dioxide were investigated near a
new 540-MW coal-fired generating station located in a rural area 25 miles north
of Madison, Wisconsin. Monitoring data for 2 yr before.the start-up in July 1975
and for the year 1976 were used to assess the impact of the plume and to investigate
the hourly performance of the Gaussian plume model. The Gaussian plume model was
successful in predicting annual average concentrations (r = 0.95), but inadequate
for simulating hourly averages (r = 0.36). The incremental annual average increase
in ambient S0? concentrations within 15 km of the plant was 1-3 yg/m .
Dry deposition of S02 was measured within the plume using the gradient transfer
method. An annual S02 dry deposition flux of 0.5 kg/hectare-year or less within
10 km of the plant was inferred, which is about 3% of the regional background
deposition.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.IDENTIFIERS/OPEN ENDED TERMS
Air pollution
Atmospheric
Deposition
Gaussian Plume Model
Meteorology
Models
Sulfur dioxide
Coal-fired power plants
Dispersion
Exhaust gases
Plumes
Portage, Wisconsin
Sulfur dioxide
c. COSATI Field/Group
13/B
04/A
06/F
07/B
18. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
19. SECURITY CLASS (This Report)
UNCLASSIFIED
21. NO. OF PAGES
114
20 SECURITY CLASS (This page)
UNCLASSIFIED
22. PRICE
EPA Form 2220-1 (Rev. 4-77)
PREVIOUS EDITION IS OBSOLETE
102
------- |