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

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                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

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                                                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

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                                  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±

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                                   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

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                                   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

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                                   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

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                                   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

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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

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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

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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

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                                    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

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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

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                               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

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                                  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-

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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-

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                                  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-

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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-

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                                  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-

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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-

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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-

-------












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-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-

-------
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                                                                      4J
                                                                     •H
                                                                      cn
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-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-

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          2200n
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 o
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        CO
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          1000
                           Stack Gas Flow
                                         Stack Temperature
              100      200       300       400       500
                       Gross  Generation In Megawatts
                                                   r290
                                                   r270
                                                         5
                                                         0)
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                Heat Input
              100       200       300       400       500
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                                                  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-

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                                      -26-

-------
              NW
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     wsw
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                      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-

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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
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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-

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             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-

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                                                          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-

-------
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                                                             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-

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                                                            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-

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                                  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-

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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-

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                    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-

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                      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-

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                    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-

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                      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-

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                      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-

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                      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-

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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-

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       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
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111
112
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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-

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172
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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
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259
260
261
262
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277
278
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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

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