United States
 Environmental Protection
 Agency
Office of Air Quality
Planning and Standards
Research Triangle Park NC 27711
EPA-450/4-89-020
NOVEMBER 1989
 Air
REVIEW AND EVALUATION
            OF
AREA SOURCE DISPERSION
       ALGORITHMS
 FOR EMISSION SOURCES
   ATSUPERFUND SITES

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                              EPA-450/4-89-020
REVIEW AND EVALUATION OF
  AREA SOURCE DISPERSION
         ALGORITHMS
   FOR EMISSION SOURCES
    AT SUPERFUND SITES
                By
        TRC Environmental Consultants, Inc.
           East Hartford, CT 06108
          EPA Contract No. 68-02-4399


        EPA Project Officer Jawad S. Touma
      Office Of Air Quality Planning And Standards
           Office Of Air And Radiation
        U. S. Environmental Protection Agency
         Research Triangle Park, NC 27711

             November 1989

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This report has been reviewed by the Office Of Air Quality Planning And Standards, U. S.
Environmental Protection Agency, and has been approved for publication as received from the
contractor. Approval does not signify that the contents necessarily reflect the views and policies of the
Agency, neither does mention of trade names or commercial products constitute endorsement or
recommendation for use.
                                      EPA-450/4-89-020

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                               ACKNOWLEDGEMENTS

    The authors wish to acknowledge the contributions of  numerous individuals,
without  whose  assistance   this   study  could  not  have   been  successfully
completed.   The Project Officer, Mr.  Jawad Touma, provided guidance  and review
comments throughout the course of  the project,  including numerous constructive
suggestions regarding  the  draft  report.   Mr.  Joseph  Tikvart,  Chief of  the
Source  Receptor Analysis  Branch,  also  provided  a  critical   review  of  the
report.  A large number of  investigators were contacted  during the  review of
technical  literature   and   recent  modeling  developments.    Dan  Reible  of
Louisiana  State University,  Jim  Bowers  of U.S.  Anny-Dugway,  and  Richard
Schultz  of   Trinity  Consultants   were  particularly  helpful  in   providing
information to aid this effort.  Dr.  Brian Lamb of  Washington  State  University
provided  valuable  information  concerning   the   forest  canopy  area  source
experiments.   The  sensitivity  analysis  plots  presented in   Section  3  were
produced through the diligent efforts of Ms.  Maureen Hess.
                                    -111-

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                               TABLE OF CONTENTS

SECTION                                                                    PAGE

                  ACKNOWLEDGEMENTS	        ii

  1.0             INTRODUCTION	       1-1
      1.1           Approach	       1-1
      1.2           Identify Superfund Site Area Source
                      Characteristics 	       1-2
          1.2.1       Source Site and Shape Considerations	       1-3
          1.2.2       Environmental Influences  	       1-7
          1.2.3       Hunan Activity	       1-7
          1.2.4       Summary of Findings	       1-8

  2.0             REVIEW OF AREA SOURCE DISPERSION ALGORITHMS ....       2-1
      2.1           Introduction	       2-1
      2.2           Overview of Area Source Algorithms	       2-2
          2.2.1       Virtual Point Source  	       2-2
          2.2.2       Point Source Array  	       2-3
          2.2.3       Line Source Segment(s)	       2-3
          2.2.4       Line Source Integration	       2-4
      2.3           Existing Area Source Models	       2-6
          2.3.1       Industrial Source Complex Short-Term Model  .  .       2-7
          2.3.2       Fugitive Dust Model	       2-7
          2.3.3       Point, Area, Line-Source Model  	      2-10
          2.3.4       Gaussian-Plume, Multiple Source Air Quality
                        Algorithm	      2-10
          2.3.5       SHORTZ Model	      2-12
          2.3.6       TEM/PEM	      2-13
          2.3.7       ISCLT	      2-13
          2.3.8       Climatological Dispersion Model 	      2-14
          2.3.9       LONGZ Model	      2-16
          2.3.10      VALLEY Model  	      2-16
          2.3.11      Air Quality Dispersion Model  	      2-18
          2.3.12      Area Source Screening Techniques  	      2-18
          2.3.13      Toxic Release Models  	      2-19
          2.3.14      Other Existing Models 	      2-21
      2.4           Overview of Area Source Dispersion Literature .  .      2-21
      2.5           Limitations of Existing Models  	      2-24

  3.0             ANALYSIS OF MODEL PREDICTIONS FOR EXAMPLE
                    APPLICATIONS  	       3-1
      3.1           Approach	       3-1
      3.2           Tests of Mathematical and Physical Principles .  .       3-3
      3.3           Predicted Concentrations for Base Case	       3-8
          3.3.1       Short-Term Models 	       3-8
          3.3.2       Long-Terra Models  	      3-10
      3.4           Tests of Mathematical and Physical Properties .  .      3-11
          3.4.1       ISCST	      3-11
          3.4.2       FDM	      3-13
          3.4.3       PAL	      3-14
          3.4.4       RAM	      3-14
          3.4.5       SHORTZ	      3-15
          3.4.6       ISCLT	      3-17
                                      -v-

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                              TABLE OF CONTENTS
                                  (CONTINUED)

SECTION                                                                    PAGE

          3.4.7       CDM	      3-17
          3.4.8       VALLEY	      3-18
      3.5           Discussion of Results	      3-19
          3.5.1       Short-Tern Results	      3-20
          3.5.2       Sector Average Models 	      3-22

  4.0             COMPARISON OF MODEL  PREDICTIONS WITH EXPERIMENTAL
                    DATA	       4-1
      4.1           Database Description   	       4-1
      4.2           Results	       4-5
          4.2.1       Unpaired Comparisons	       4-6
          4.2.2       Paired Comparisons	       4-6

  5.0             CONCLUSIONS AND RECOMMENDATIONS 	       5-1
      5.1           Conclusions	       5-1

                  REFERENCES	       R-l
                                      -VI-

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                                LIST  OF FIGURES

FIGURE                                                                     PAGE

 2-1          SUBDIVISION OF A SQUARE AREA SOURCE INTO FIVE LINE
                SOURCE SEGMENTS NORMAL TO THE WIND DIRECTION  ....       2-5

 2-2          SUBDIVISION OF RECTANGULAR AREA SOURCE INTO LINE
                SEGMENTS BY PAL	      2-11

 2-3          ILLUSTRATION OF SECTOR INTEGRATION USED IN CDM  ....      2-15

 2-4          SCHEMATIC OF THE VIRTUAL POINT SOURCE AS PROJECTED FROM
                AN AREA SOURCE	      2-17

 3-1          AREA SOURCE AND RECEPTOR CONFIGURATION FOR BASE CASE
                SCENARIO	       3-2

 3-2(a)       ILLUSTRATION OF INFLUENCE ZONE FOR NEAR-FIELD RECEPTORS       3-5

 3-2(b)       SUBDIVISION OF BASE CASE AREA SOURCE	       3-6

 3-2(c)       SOURCE-RECEPTOR CONFIGURATION FOR DIAGONAL WIND
                DIRECTION	       3-7

3-3 to 3-47   GRAPHS OF CENTERLINE CONCENTRATION VS. DOWNWIND
                DISTANCE FOR SELECTED MODELS, METEOROLOGICAL
                CONDITIONS AND SOURCE CONFIGURATIONS  	  3-23 to 3-67

 4-1          ISOPRENE FLUX EXPERIMENT SAMPLING GRID, RELEASE POINTS
                AND WOODLOT	       4-3

 4-2          RANGE OF MAXIMUM PREDICTED AND OBSERVED CONCENTRATIONS
                FOR TRACER RELEASE EXPERIMENTS  	       4-7
                                     -vi i-

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                                LIST OF TABLES

TABLE                                                                      PAGE

 1-1          AREA SOURCE CHARACTERISTICS	       1-4

 2-1          CHARACTERIZATION OF AREA SOURCE ALGORITHMS IN EXISTING
                MODELS	       2-8

 3-1          SUMMARY OP SENSITIVITY TEST RESULTS FOR SHORT-TERM
                MODELS	      3-21

 4-1          METEOROLOGICAL AND EMISSION CHARACTERISTICS FOR TRACER
                RELEASE EXPERIMENTS IN A FOREST CANOPY  	       4-4

 4-2          MAXIMUM OBSERVED AND PREDICTED NORMALIZED TRACER
                CONCENTRATION FOR EXPERIMENT  	       4-8

 4-3          SUMMARY OF RELATIVE DIFFERENCES BETWEEN OBSERVED AND
                PREDICTED MAXIMUM VALUES  	      4-10
                                     -ix-

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1.0 INTRODUCTION



    An evaluation of  dispersion modeling  techniques  available  for  estimating



ambient  concentrations  produced  by  emissions  from  sites contaminated  with



toxic pollutants  has  been conducted.   The focus  of  this  study has been  the



application of  area  source  dispersion algorithms  to these emission sources.



These sources can be  characterized  as  low level releases with little buoyancy



due  to  either  momentum  or temperature.   Potential applications  include  both



the estimation  of short-term concentrations  associated with  site remediation



activities  and the   calculation  of  long-term  concentrations  for  estimating



population exposures  in the  vicinity of a landfill,  lagoon or  waste disposal




facility.








1.1 Approach



    The  study  consisted  of  four  tasks.   The  report has  been  organized to



present the results of each task in sequence.



    Task  1 -  Identify Source  Characteristics.  The  emission characteristics



representative  of  superfund/landfill   sources  were  examined  to  identify



modeling  requirements and related  technical  issues associated with estimating



ambient  concentrations  near these  sites.   Task  1 findings  are  described in



Section 1.2.



    Task  2 - Review  Available  Area Source  Models.  Existing  models and  the



technical  literature  were reviewed to identify available  modeling techniques



for  estimating  short-term and  long-term concentrations due  to  area sources.



Specific  models were  selected for further evaluation, based on  their potential



suitability   for   landfill/superfund  applications.    Task  2   findings   are



described in Section 2.



    Task  3 - Analysis  of Model  Predictions  for  Example  Applications.   Five



short-term area source  models and three long-term (sector average) models were






                                      1-1

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applied to a number  of  example applications in order  to compare the magnitude



of  concentration  predictions  and  to  test whether  near-field  and  far-field



concentration  estimates  were  consistent  with  mathematical   and  physical



principles.  Task 3 results  are presented in Section 3.



    Task 4 -  Comparison of  Model Predictions  with  Observed  Concentrations.



The five short-term models were applied to estimate ambient  concentrations for



a  series of  tracer dispersion experiments involving low-level  releases within



an isolated grove of trees.   These  experiments simulate an area  source release



over  an   area  of  15,000   square  meters.    Predicted  and  observed  tracer



concentrations were  compared  for   thirteen one-hour  experiments  at  sampler



locations  approximately  100 m  downwind  of the source region.   Task 4 results



are summarized in Section 4.



    Conclusions and recommendations from this study are discussed in Section 5.








1.2 Identify Superfund Site  Area Source Characteristics



    Landfills  and other large  area  sources have characteristics which have a



substantial  bearing  on the  air  quality  modeling method  used  to  simulate



pollutant  dispersion  from such sources.   Many of the  currently  used pollutant



dispersion models are based on a Gaussian  formulation  originally developed for



point  sources.   Over  time,  the  model  formulations  have  been  adapted  for



application  to  distributed  sources  (line   sources,   area  sources,  volume



sources)  but many of the  original  point source  assumptions are  retained in



these  models.   The purpose  of this discussion  is  to describe  the  types of



emissions  from   superfund   sites   that  may  be  modeled   as   area  sources.



Subsequent sections  of  this report will provide  detailed descriptions  of the



current model formulations used to predict dispersion from these area sources.



    Various  types of toxic  waste sources  fall into  the area  source category.



These  include  landfills,  waste  lagoons,  evaporation  and  settling  ponds,





                                      1-2

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agricultural fields which have  been treated with chemicals,  and regions where



long-tern exposure  to toxic  chemicals  (due  to  manufacturing,  mining,  power



generation,   etc.)   has  contaminated  the  soil.   For all  of  these  sources,



pollutants are emitted at or near  ground  level.  The  sizes  of  these  sources



can  range from a  few square meters  in the  case of settling  ponds to  a few



square kilometers  in the case  of  contaminated  soils.   The  effect of  these



sources  on   the  population  also  varies  based  on source  size, strength and



type.  Many  sources  are  located in areas remote  from the  general  population.



Others, by their nature,  occur  in centrally  located  areas  where the potential



for exposure is large.  Emissions  from many of these sources  are  a  function of



human  activity.    Addition  of material,   maintenance   or  disruption  of  the



surface  layer will  have  a  bearing  on emissions.   Emissions  may  also  be



affected  by  environmental  conditions  such  as wind speed,  air  temperature,



ground surface temperature,  and surface moisture.








    1.2.1 Source Site and Shape Considerations



    Estimating concentrations from  surface  area  sources  presents a  number of



challenges  for  the  modeler.  Available  area  source   algorithms  contain  a



variety of  assumptions  and  offer  different  strengths  and  weaknesses.   More



reliable concentration estimates  can  be obtained by careful problem definition



and  by  selection   of the  most  appropriate  model/algorithm.   A  summary  of



superfund  site area source  characteristics   is included  as Table  1-1.   The



location,  geometry  and  relative  elevation  of  a  typical  waste  storage  or



landfill site  are   important  factors  in the  dispersion  characteristics  of the



site.  Gaussian  dispersion  models  generally  assume  an  elevated source  which



injects  pollutants  into  a  moving  air  stream.   Since  many  landfills  are



low-lying or in pits, the mechanisms  by which  pollutants enter  the atmosphere



are  more  complicated.  The  rate  of  entrainment  of pollutants  which exit the






                                      1-3

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                          TABLE 1-1

                 AREA SOURCE CHARACTERISTICS



-  spatially continuous emissions (non-point)
   unique   site  geometry   including   depressions,   piles,
   lagoons, etc.
   temporally variable  source strength  dependent on  ambient
   atmospheric conditions  (primarily  temperature,  wind speed,
   precipitation) and maintenance activity levels
-  surface sources characterized by low wind speed at surface


-  density flows may be important over surface area sources
   chemical  type  of   emission   may  vary  spatially  across
   landfills
                             1-4

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surface will be controlled  by diffusion,  mechanical  (turbulent) flushing  and



suction effects.  In the  case of a surface source, the horizontal  velocity at



the surface is zero ("no slip" boundary condition) and the rate  of  emission of



the pollutant is determined  by the diffusive  velocity,  which is based on vapor



pressure gradients and  boundary  layer resistance.  The  near surface  layer is



characterized by  low  mean wind  speeds (often less than  1 m/s).  The Gaussian



models generally avoid extrapolating wind speeds down to  the surface, however,



because  low wind  speed values  can  cause unrealistically  high concentration



predictions.  Most models specify a minimum 1.0 m/s wind  speed  for  calculating



concentrations.



    Many  modern  landfills  have  substantial  vertical  extent  (>10  m)  and



therefore  may  be  modeled  as  elevated  area  or volume  sources.    Since  the



landfill  is projecting into  the  flow,  the  ambient  wind  will ventilate  the



surface  layer of  the  landfill.   The actual  height  is physically  limited by



base  area  of  the landfill  and  the  angle  of  repose   of   the  material.   In



practice, the maximum  height may be  dictated by  regulation and/or  constrained



by the  need for access by heavy  machinery.   In the case  of a  large elevated



landfill,  the source  itself may  be  an obstacle to the  flow and establish the



flow field  for some distance to the leeward side of the landfill.   The modeler



may  account  for  this  effect  by  using  existing building wake  or downwash



algorithms.   However,  the  elevated landfill is unlike  a building  because  its



sides  are  not  vertical  and its  entire  surface  area  (top  and sides)  may be



emitting  pollutants.    The  sloping   sides of  a  landfill  represent  a  more



"aerodynamic" shape and are likely to produce  less turbulence  than a building



with  vertical  walls.    Since the landfill  flanks  are  angled,  the  source



strength  per horizontal area of  the  flanks  may be  greater than  that  of the



top.  Elevated  landfills may have  a region of high concentration in the  lee of
                                      1-5

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the  landfill  pile  with  dilution  primarily due  to  wake  eddies  entraining



unconcaminated air from windward of the landfill.



    The  estimation  of  emissions  and  dispersion is  also  complicated if  the



source is below the  local  land surface elevation.  Situations  in which trash,



waste sludge,  etc.  are placed  in a pit  in  the earth  are  common,  and  such



situations may require  emission and/or dispersion algorithms to treat a sunken



source and a cavity flow problem.   With existing algorithms,  the  sunken source



may  be  approximated  by a  surface source  of  lesser  strength.   The  rate  of



pollutant emissions  to  the ambient air will depend  not only  on the  rate  of



emissions from the ground  surface into the  air  within the pit,  but also on the



rate of exchange (ventilation)  between  the pit and the  air above it.



    In all  of these  scenarios,  it may be  useful to  characterize mixing  and



surface  conditions  and  use  these characterizations  in the  calculation  of



source strength.  Unlike the combustion effluents created by manufacturing and



power production  which are proportional  to  the  production  rate,  the  source



strength  of  a  landfill  or other area  source  is  more  directly related  to



environmental conditions.   Under  conditions  of  limited mixing,  the  partial



pressure  gradient  over  the area  source is  reduced  and volatilization of  a



given chemical decreases.   When mixing  is enhanced,  partial  pressure  gradients



are  steep  and the source  strength increases.  Snow cover may  effectively  cap



an area source and rainfall greatly reduces  emissions.



    In the  situation of a  low lying  or sub-surface source  with emissions  at



ambient  temperature  (neutral  thermal   buoyancy)  the worst  case  concentration



scenario will be  a  surface-based  inversion  where a  relatively shallow layer of



dense, cold  air sits on  the  surface  of  the source  providing  a thin  mixing



layer and high  concentrations.  It could be expected that this  effect would be



local because  wind speed  and  transport  are limited  in most  strong  surface



inversions.  Conversely,  the  fact  that the air in  these inversions  is denser






                                     1-6

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near  the  surface  allows  density flows  to develop.   In  regions of  complex



terrain  this  effect could  lead  to anomalously  high  concentrations  down  the



hill from  a source.   Air "draining" from a source of toxics and "pooling" over



a low-lying area could lead to high exposures.








    1.2.2 Environmental Influences



    The source strength of an area source may be strongly correlated  to source



surface  temperature.   Chemical  reactions  and  decay  rates  of  the  emitted



pollutant may also depend on temperature.   In  the case of an  area source such



as a  landfill,  many gaseous emissions occur by evaporation.  Evaporation rates



are a function of the ground surface temperature.  For many organic compounds,



evaporation rate  is strongly  dependent on temperature across ambient ranges.



After emission, some evaporated compounds undergo chemical  reactions  which are



strongly   temperature   dependent.     In   general,   algorithms   to   estimate



evaporation rates  and decay  rates as  a  function of  environmental conditions



are not yet available.








    1.2.3  Human Activity



    The  degree  to  which area source strength is coupled to the environment may



depend  on the  surface  characteristics of  the  source.   In  the  case  of  an



abandoned  site,  the source surface may be  capped and/or vegetated which would



lower  source  strength sensitivity  to ambient  temperature  and  wind.   In the



case  of a cap, source  strength  would be  largely determined  by sub-surface



diffusion.   In  an  active  landfill,  material  may be periodically  "turned"



causing  source  strength to  be dependent  on both  the ambient  environmental



conditions and on  landfill  operation or clean-up schedules.



    The  source  emissions  from  many  large  area  sources   will  be  spatially



inhomogenous  with  respect  to  both  strength  and  type.   For  example,  at   a






                                       1-7

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typical  landfill,  different  types  of waste   (household  trash,  construction




waste, industrial waste,  etc.)  will  be dumped  in  different  areas, and dumping



will occur in one area while grading occurs in  another.  These  differences are



often  important  for  estimating peak  short-term  concentrations, but  are less



important on an annual basis.








    1.2.4 Summary of Findings



    Air pollutant emission sources at sites contaminated with toxic pollutants



are typically  low-level,  distributed  sources,  ranging in  size from  about  10



square meters to several square kilometers.  Considerable spatial  variation in



emission  rates  across  the  source  area  is  common,  particularly for  larger



areas.    Both   short-term   (one-hour)   and   long-term   (annual)   average



concentration  estimates  are needed,  depending  upon the  specific  application.



Typical source-receptor distances for  short-term  concentration  estimates range



from  10  m to 1000 m and  may include "on-source"  receptors (to estimate worker



hazard)  and "fenceline"  receptors  immediately adjacent to the  contaminated



area.  For  long-term  average estimates, population exposure  is  most  often the



primary  concern.   Typical  source-receptor distances  for long-term  estimates



range from several hundred meters to about 10 km.



    Source  geometries encountered  at  some  contaminated  sites pose  special



challenges   for   air   quality  modeling.    Elevated  sources  (e.g.,  elevated



landfills),  below-grade   sources  (e.g.,  excavated areas),   and small  heavily



contaminated   areas   "nested"   within  larger  source  regions  all  represent



difficult scenarios for many existing area-source models.



    Source  emission  rates at contaminated sites often  vary  with environmental



conditions  and as a  result of human activities.   Many  of  the  same  factors



which  affect  emissions  will  also  influence  dispersion  behavior.   It  is



therefore  important  to   account  for  these  variations  when  performing  air



quality modeling analyses.




                                      1-8

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2.0 REVIEW OF AREA SOURCE DISPERSION ALGORITHMS



2.1 Introduction



    Accurate  prediction  of  ambient  concentrations  resulting from  pollutant



emissions  from  area  sources   is   a  problem  of   considerable   importance.



Spatially distributed  sources  of pollution include  landfills,  settling ponds,



lagoons, agricultural  fields  and storage  piles.   Residential and  commercial



urban  areas  may  also  be  considered  as  area  sources,  although  emissions



actually originate from many small point sources.



    The  most widely  used  air  quality models  are those  incorporating  the



Gaussian plume equation  to  describe the transport and dispersion  of emissions



from a  point  source.   These models provide an efficient  and easily understood



calculation procedure  for estimating  concentrations.   Area source  algorithms



based on the Gaussian plume equation are  of primary interest  for this study,



since  these  algorithms  can  be  easily  incorporated  into  another  existing



model.   Area  source  algorithms  from non-Gaussian models are  not  transferable



and are not as well suited for routine applications.  Non-Gaussian area source



algorithms  were  reviewed,  however,  in  an effort  to identify any  approaches



which might  represent  significant  improvements  in calculation  accuracy  or



efficiency.



    For a Gaussian model, the concentration  from a  point source at  a receptor



can be  calculated  readily,  using a simple set of equations.   If the emissions



are spread uniformly over an area, however,  the resulting concentration can be



determined  only  by  integrating  over  the   source  area.    The  area  source



contribution  cannot be computed  exactly using simple equations.  Area source



algorithms  represent  calculation  procedures which  have  been  designed  to



estimate the area source  contribution in an efficient manner.
                                      2-1

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    Pollutant  dispersion  models  which  are  currently  available  to  predict



pollutant  concentrations  at  user-specified  receptor  locations   have  been



examined.  One  important consideration for  this study is  the  availability of



FORTRAN  source  code.   Eleven of the models  included in  the UNAMAP6  package



(EPA, 1986) contain area source algorithms.   Additional models were identified



by  contacting   researchers   active  in   developing  models   for   regulatory



applications and by searching the recent technical literature.








2.2 Overview of Area Source Algorithms



    Four basic  techniques for estimating area source  impacts were  identified.



Two  of  these  approaches  are  primarily  source-oriented,  while  two  (upwind



integration and point source array)  are  receptor-oriented.   Different models



employ variations on  a  given technique or a  hybrid of two methods.   The four



basic techniques are described below.








    2.2.1 Virtual Point Source



    The  virtual  point source method as suggested by Turner (1970)  to estimate



area  source impacts  assumes that  the  pollutant plume  downwind of  an  area



source can  be  simulated as a point  source.   The initial source dimensions are



accounted  for  by placing the point  source  upwind  of  the actual area  source



location,  so  that  the  lateral spread  of the  plume  at   the  area source  is



comparable to the source width.  The emissions  for  the  replacement  source are



set  equal  to the area  source emissions.   Thus, the equations  used  to compute



concentrations  for  an area  source are  the  same  as  those  used  for a point



source.   One  limitation inherent in this approach is  that the point source



plume  does  not  accurately  reproduce the  crosswind  concentration distribution



on the  source.  The  point source plume  distribution  is Gaussian,  with higher



concentrations  in the center and lower concentrations at  the edges, while the






                                      2-2

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actual emissions  are  uniform across  the source.   Another limitation  of this



method  concerns  the   choice  of  a  single  source  location  for  oz,  which



eliminates the  effect  of the  along-wind source distribution.   Concentration



predictions are  therefore distorted, particularly  in the  near-source region.



Virtual  point  source  algorithms  are  generally not  utilized  in  short-term



models.   For  long-term  models,  which  incorporate  a  sector-averaged  plume



width, this  technique  is more  commonly used.   Examples include  ISCLT,  LONGZ



and VALLEY.








    2.2.2 Point Source  Array



    The  receptor-oriented point  source  array  provides  a  second  method  of



estimating area source concentrations using the Gaussian plume  equation  for a



point  source.   Using  this  approach,  the  model generates  an array  of  point



sources centered on the receptor.  The total area of the region  covered by the



source array is  apportioned  among the  point  sources,   so  that  each  point  is



representative of its  surrounding area.   The emissions due  to area sources are



then  apportioned  to the point  source   array,  based on the location  of each



point  source and  the  size  of  the  area  it represents.   Two  of the  models



reviewed  for this  study calculate  area source contributions  using  a  point



source array.   CDM,  a sector-average  model, generates  a  radial  source  array



around  each  receptor  based  on  the  22.5°  sectors  which  it  employs  for



calculating  concentrations.    The  Texas   Episodic   Model   (TEM)    and  its



derivative, PEM, generate a rectangular  source array around each receptor.








    2.2.3 Line Source  Segment(s)



    For a  Gaussian model,  the concentration calculation for an area source can



be simplified  if  the  source  area is simulated as one  or more  line  segments



oriented normal to the wind direction.   The computation effort  required  for a






                                      2-3

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line source  with uniform emission  density is  roughly equivalent to  that for



two or  three point sources.   A schematic  diagram is  provided in  Figure 2-1



illustrating the simulation of  a square area using five  line source segments.



If the endpoints of each segment correspond  to the  boundaries of  the  source



region,  the simulated  source  will  provide  a reasonable   representation  of



source geometry.  The number  of segments used by  a model represents  a  choice



between accuracy and efficiency.  Depending upon the number  of lines  employed



and the positioning of  the  lines within the  source area, the collapsing  of a



two-dimensional area into a  line may significantly distort the source-receptor



geometry.   This problem is  most  significant  in  the  near-field  and  most



pronounced  with algorithms   that  use  single  line  segments  to represent  an



area.  The  majority of  the  short-term models  reviewed  for this study use the



line source segment approach.   Examples include ISCST, FDM, PAL, and SHORTZ.








    2.2.4 Line Source Integration



    The use of  line source  segments normal  to the wind direction  provides a



convenient   method   for  simulating   area   source   contributions,    but   the



computational  effort can be  substantial if each area source is subdivided into



many  line  source  segments.   Certain  simplifying  assumptions  can  greatly



enhance  computational  efficiency by  accounting for  the  contributions of many



line  segments  within a single  calculation.   Two  techniques  were  identified



which represent  integration over many line segments.



    Crosswind  Integration.  In urban regions, the emissions  density generally



does  not  change abruptly between  adjacent  area  sources.   If the emissions



density  is assumed to  be constant in  the  crosswind  direction,  area  source



contributions  can be  calculated based only on the emissions from sources along



a line  directly upwind  from  the  source.   The  RAM  model   incorporates  this



"narrow   plume  hypothesis"   to  compute  area   source   contributions.  This






                                      2-4

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          WIND
       DIRECTION
LINE SOURCE
FIGURE 2-1.
SUBDIVISION  OF  A  SQUARE  AREA  SOURCE INTO
FIVE  LINE SOURCE SEGMENTS NORMAL TO THE
WIND DIRECTION
                    2-5

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technique  will  give  accurate  results  only  if  the  emissions  density  is



relatively  uniform  over  a  crosswind  distance  comparable  to  the  upwind



source-receptor   distance.    Concentration  calculations   do   not   account



accurately for  source-receptor geometry  for  a single  (isolated) area  source



square.



    Upwind Integration.   For receptors  located  within an  area  source or  at



near-field distances,  the region upwind  of a receptor is often  occupied  by a



single area  source.   In  this  situation, the  crosswind term  in the  Gaussian



equation  integrates  to a  constant,  independent of  downwind distance.  For a



ground-level release  height, it  then  becomes possible  to  integrate  over the



source area  in the  upwind direction.   This  technique is only  appropriate at



near-field distances  and only  for receptors  downwind of  the  center of the



source area.   Both SHORTZ  and LONGZ  employ  upwind integration  at  near-field



distances to calculate the vertical dispersion term in the Gaussian equation.








2.3 Existing Area Source Models



    Model  algorithms  for area  sources  are   contained in  dispersion  models



designed  for a wide  variety of applications.   The  primary  focus of this  study



is  area  source  algorithms  suitable  for  estimating  concentrations  due  to



routine  (non-accidental)   air  emissions from Superfund sites and  other   areas



contaminated   with   toxic  materials.     Models   designed   for   regulatory



applications to estimate  concentrations for time periods ranging from one hour



to one year over distances between  10  m and 10,000 m  are most relevant.   Area



source  algorithms  in  five  existing  short-term  models designed  to predict



concentrations  based on hourly meteorological  conditions  were reviewed (ISCST,



FDM,  PAL, RAM, SHORTZ).   Six long-term  (sector-average)  models  designed to



predict  concentrations based on  the  frequency distribution  of  meteorological
                                      2-6

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conditions were  also  reviewed  (ISCLT,  FDM,  CDM,  VALLEY,  LCNGZ,  AQDM).   A



number of other models  designed for air toxics or other applications were also



included in the review of area source algorithms.   Table  2-1  characterizes the



area source algorithms in some widely used models.








    2.3.1 Industrial Source Complex Short-Term Model



    I SCSI uses  the finite  line  source approach  to model area sources.  Each



square area source is modeled as a single line segment oriented  normal  to the



wind  direction.   The  line  segment  length  is  the  diameter  of  a  circle



containing the  same area  as  the  square.   For estimating lateral  dispersion,



the  line  source  is located at the downwind edge of the  area  source.   For the



vertical dispersion coefficient  oz,  ISCST uses a "virtual distance"  Xy =  X +



XQ,  where  X is the along-wind  distance  from the  downwind  edge  of the  area



source to the receptor, and XQ is the side length of the area source square.



    The ISCST  algorithm predicts zero concentration for a  receptor  located



within an area  source.   The ISC  User's Guide  recommends  that receptors not be



placed  within  a   distance  of  one  side   length from  any  area  source.   If



receptors are placed at closer distances,  the source(s)  should be subdivided.



    The  ISCST  area   source   algorithm   does   not  accurately   account  for



source-receptor geometry.   The  use of a single line source segment to simulate



a square area eliminates the along-wind distribution of source emissions.  The



length of the  line segment used by ISCST is independent of wind direction, but



the  crosswind  extent of an area  source  square changes  with  wind  direction.



The effect of these simplifications is greatest at near-field receptors.








    2.3.2 Fugitive Dust Model



    The Fugitive  Dust Model  (FDM) was developed to model both short-term and



long-term  average  particulate  emissions  from  surface  mining  and  similar






                                      2-7

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                                  TABLE 2-1

                  CHARACTERIZATION OF AREA SOURCE ALGORITHMS
                              IN EXISTING MODELS
          Model
      Area Source Algorithms
    (a)  Short-Term Models

         ISCST


         FDM

         PAL

         RAM


         SHORTZ*
single  line  segment  with  virtual
distance for vertical dispersion

multiple line segment

multiple line segment

upwind  integration (infinite  line
source)

line segment
- near-field: upwind integration
- far-field: single line segment
    (b)  Long-Term (Sector-Average) Models
         CDM


         ISCLT

         FDM

         LONGZ*


         VALLEY

         AQDM
upwind  integration  (point  source
array)

virtual point source

virtual point source(s)

- near-field: upwind integration
- far-field: virtual point source

virtual point source

virtual point source
* ground-level source height only
                                      2-8

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sources.  FDM accounts for  deposition losses as well as  pollutant dispersion.



The  area  source  algorithm  is  designed for application  to rectangular  area



sources  with   principal   axes  oriented   north-south  and   east-west.    FDM



subdivides each area source into five line source segments  oriented normal to



the  wind direction  (see  Figure 2-1).   For  short-term   averages,   each  line



source is then modeled using the CALINE line source algorithm, which has been



incorporated into FDM.



    CALINE is a line source  model developed by the California DOT  to determine



the  dispersion of pollutants from  highways.  For each line segment, FDM/CALINE



predicts the  concentration  using  the Gaussian model  equations  for a  finite



line source.  Each line  segment contributes based on its  upwind and crosswind



distance from the receptor and on ov and oz.  On-source receptors  are allowed,



but  only  the line  segments upwind  of  the receptor  influence the  receptor.



FDM/CALINE  requires  a  computational  effort  for each  area  source which  is



roughly 10 times the computational  effort for one point source.



    The FDM/CALINE  model uses the  Pasquill-Gifford dispersion  coefficients,



with adjustments to  az  to account  for the  initial  dispersion  associated with



vehicle-induced turbulence and the  buoyancy of vehicle exhaust  emissions,  plus



averaging-time adjustments  to  ay.   Consequently,  the ay and oz values used by



FDM are larger than the unadjusted Pasquill-Gifford coefficients.



    For long-term averages, FDM employs a sector average  treatment  of lateral



dispersion.   Each area  source  is again divided into five  line  segments normal



to the  wind direction.   The  emissions from the  area source   are  apportioned



among the five  line  segments  in proportion  to their  lengths.   Each segment is



then modeled  as a virtual  point  source,  located at the  center  of the  line



segment.  The Pasquill-Gifford  oz  coefficients are used without adjustment for



long-term average calculations.  For long-term averages,  FDM was  judged  to be



similar to ISCLT and was not evaluated separately.






                                      2-9

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    2.3.3 Point,  Area,  Line-Source Modal



    The  Point,  Area,  Line-Source Model  (PAL)  Version  2.0 is a  steady-state



Gaussian plume model.  This model is recommended for source  dimensions  of tens



to hundreds  of meters.  The  area source algorithm uses multiple  line  sources



arranged perpendicular to the  wind to simulate a rectangular area source.



    The program initially  calculates  concentrations from 9 line sources spaced



equally  across the source  area.   The Gaussian  model  equations  for a  finite



line  segment are  used.   Next,  PAL computes the  concentrations  from  10 line



segments located midway  between  each pair of the  original nine lines  and the



two corners of the source area (see Figure 2-2).



    PAL  estimates  the  accuracy  of the computed  concentrations,  based  on the



difference between the two concentration estimates (Cg - CIQ) divided by the



average  %  (Cg + CIQ).   If this  relative difference  exceeds  a user-specified



accuracy  limit,  PAL continues with  further  calculations.  (The  User's Guide



recommends an  accuracy  limit  of  .02.)  For the  third  iteration,  20  lines are



placed  midway  between  the  19  lines  (and  two  corners)  used  previously.



Iterations continue until the accuracy limit is satisfied.



    PAL  requires a computational effort for each area source which is at least



20  times  the  effort   for  a  point  source.    PAL  Version  2.0  uses  the



Pasquill-Gifford   rural  dispersion  coefficients  when  the  rural  option  is



selected.








    2.3.4 Gaussian-Plume, Multiple Source Air Quality Algorithm



    The  Gaussian-Plume  Multiple  Source  Air  Quality  Algorithm   (RAM)  is  a



steady-state Gaussian  plume  dispersion model.   RAM was developed  for  use in



urban and rural areas with  low relief.



    The  area source algorithm in RAM calculates  concentrations  for a  grid of



square  area sources based upon  contributions from  sources  located directly






                                      2-10

-------
                              FIGURE 2-2
                  Subdivision of Rectangular Area Source
                     Into Line Segments  by PAL
                  WIND
     AREA
    SOURCE
          10  LINE   SOURCES  LOCATED   MIDWAY
          BETWEEN ORIGINAL 9 LINE SOURCES AND
          CORNERS
                                                        ©
                                                    RECEPTOR
IF *10 DIFFERS FROM THE AVERAGE
OF x9 AND *10 BY LESS THAN THE
TEST VALUE THEN RESULTS ARE
AVERAGED AND NEXT AREA SOURCE IS
ANALYZED
                                  2-11

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upwind   of   the   receptor.     This   algorithm  uses   the   "narrow   plume



simplification," which assumes that  the emissions density  directly  upwind of



the receptor is  representative of  emissions at all crosswind  locations.   This



assumption allows the concentration  at  a receptor to  be  calculated  using the



Gaussian   model   equation  for   an  infinite   line   source.    RAM   computes



concentrations due to area sources along the path upwind of the  receptor using



a  distance  spacing which  starts  at  10   m for  near-field  calculations and



increases  in four steps  to a maximum of  1,000  m at  distances  beyond  15 km.



The  narrow plume hypothesis  is  most  suitable  in situations where  there is



relatively little lateral variation  in  source strength,  such as  an  extensive



urban area.   In the  case  of an  isolated  area source, this  simplification is



not  valid, except  for  near-field  receptors  downwind of  the center of the



source.








    2.3.5 SHORTZ Model



    SHORTZ  is  a short-term  dispersion  model  suitable  for  use  in flat or



complex  terrain.   SHORTZ  calculates  concentrations  for  ground-level   area



source  squares  utilizing  a finite  line  source  approach.   Beyond a distance



equal to  3 times the source width,  the area source  is  simulated as  a  single



line  source  segment oriented  normal to the  wind  direction.   The length of the



segment  is  the  crosswind  projection  of  the  source area.   For  near-field



receptors,  the vertical term  is calculated  as  an integral  from the downwind



edge  to  the upwind edge of the  source.  The  lateral  term is calculated  with



the  line segment located at  the center of  the  source area.   (This separation



of the lateral and  vertical terms in the Gaussian  equation  is  only correct for



receptors  downwind  of   the source  center.)  SHORTZ  accept  a  user-specified



"source height"  to  produce  an  initial az for  area sources,  but  a ground-level



release  height is always assumed.  The dispersion coefficients  used  in SHORTZ






                                      2-12

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are  based  on  the  Pasguill-Gifford  dispersion  curves,  but   they  do  not



correspond to the Pasquill-Gifford ay and az values used in EPA models.








    2.3.6 TEM/PEM



    The  Pollution Episodic  Model  (PEM)  is  a  Gaussian  plume  model  which



includes deposition/settling and  first  order chemical transformations.  PEM is



an urban scale  model and  is  designed  to  incorporate multiple-point  and area



sources.  It  is designed to calculate  short-term, ground level concentrations



and  deposition  velocities.   PEM incorporates  the  framework  of  the  Texas



Episodic Model  (TEM) and  the  TEM area source treatment.  PEM and  TEM  use a



point source array to estimate area source concentrations.  The emissions from



area  sources  are  apportioned  among  a  rectangular  array  of  points.   The



distance spacing of  the point  array is  specified by  the user.  Area  source



contributions  are  calculated  only  for  sources located within  8  distance



increments of a receptor.  This area source  technique has serious limitations,



and the TEM model developer does not recommend  the  use of this model for area



sources.  Based upon the initial review,  neither PEM  nor TEM was  chosen for



further evaluation in this study.








    2.3.7 ISCLT



    The algorithm used  in  ISC  long-term {ISCLT) to model square area sources



is a  virtual  point source  approach.  The  concentration calculations  in ISCLT



are  based  on  a  22.5°  sector-average  plume  width.    The  distribution  of



concentration is smoothed between radial sectors using  a  smoothing  function to



produce  a   continuous   distribution  of  concentration  vs.   direction.   For



estimating  lateral dispersion,  the  location of..the  virtual  point  source  is



displaced upwind of the area  source center so that the sector width  at the



source is equal to the diameter of a  circle  with the same  area  as  the square.






                                     2-13

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For estimating vertical dispersion, the  point source is located  at  the center

of the source area.



    2.3.8 Climatoloqical Dispersion Model

    The  Climatological Dispersion Model  (CDM)  is  used  to  model  long-term

average pollutant concentrations  (seasonal or  annual)  using  average emission

rates  and a  joint  frequency  distribution of wind  direction, wind  speed and

stability.

    The COM area  source algorithm is designed to  calculate concentrations for

a grid of area source  squares.  CDM generates a radial array  of  points around

each  receptor based  on angular and radial spacing (DINT and DELR) specified by

the  model user.  CDM  overlays  the  polar  coordinate  point  array  over  the

square  area  source  grid  and assigns  emissions  to  each  point based upon its

location  and the area  (in  the  polar  coordinate  array)  that  each  point

represents.  The  process  is illustrated in Figure 2-3.   The point source array

is  then  used to calculate  concentrations.   CDM  allows  a  minimum  angular

spacing  of 1.25° and  a minimum  radial  spacing of  10  m  for  the point array.

The radial spacing increases with distance according to the following table:


                    Radial Distance          Radial Spacing

                        < 2500 m                  DELR
                       2500-5000 m              2 * DELR
                         >5000 m                4 * DELR


    CDM  computes contributions for a maximum of  100  radial  increments.   The

size  of an area source  and  its distance from the receptor  can  only be  resolved

in  multiples  of DELR.  A small  radial  spacing  provides  improved  spatial

resolution but reduced distance range... If DELR is 25 m,  the distance range is

2.5  km.   Conversely,  as DELR increases the  spatial resolution  of  the point

array decreases.
                                      2-14

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       la.
                                                    „  EMISSION
                                                        GRID
Figure 2- 3
Illustration of sector  integration used in COM.  In this figure, R is
receptor location, Pm is  the maximum distance to the edge of the grid
from the receptor, DELR is  the upwind step width and n is the step
number,  (reproduced from  Irwin et al., 1985)
                                    2-15

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    2.3.9 LONGZ Model



    The LONGZ  model  is  the  long-term  average version  of  SHORTZ.   The  area



source algorithm in  LONGZ  is  designed for application to  square,  ground-level



area sources.  A virtual point source approach is used for  distances  beyond 3



source widths.  The  source location is  displaced upwind for simulating lateral



dispersion in a manner identical  to ISCLT.   For vertical dispersion,  the point



source is located at the area source center.



    For   near-field   concentration   estimates,   LONGZ   calculates   vertical



dispersion based on  the  integral of the vertical term in the Gaussian equation



between  the  downwind  edge and  the  upwind  edge  of the   source  area.   The



procedure  for  lateral   dispersion  is  not  affected.  Aside from  differences



related   to   dispersion  coefficients,   LONGZ   closely   resembles   ISCLT.



Consequently, LONGZ was not evaluated separately in this study.








    2.3.10 VALLEY Model



    The VALLEY  model was developed primarily to simulate dispersion from point



sources in the deep valleys typical of mountainous  terrain.   The  model employs



a  sector-average  treatment  of  lateral  dispersion  to  simulate  worst-case



dispersion   conditions.   The  area  source   algorithm  used   in   this  model



incorporates the virtual point source approach for  a square area source.  For



vertical  dispersion,  the area source is modeled  as if the  emissions  were due



to a point source located at the center of the source area.



    For  lateral  dispersion,   VALLEY treats  three  types  of  source-receptor



relationships:   (1)  far-field,  where the  entire source area contributes to a



given  receptor;  (2) near-field, where only  a  portion  of the  source  area



affects  a given receptor;  and (3) the receptor is inside the source area.  The



first  two cases are  illustrated  in Figure 2-4.
                                      2-16

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VIRTUAL SOURCE
LOCATION FOR A
                                 A' • AREA "SEEN" BY
                                    RECEPTOR 2

                                         I
         22.SO SECTOR
                                                                      WIND
                                                                    DIRECTION
                                                                  RECEPTOR 1
                       SOURCE AREA A
                       (TOTAL SQUARE)
  Figure  2- 4
Schematic of the virtual  point source as projected from an
area source, (reproduced  from Burt,  1977)
                                  2-17

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    In the first case.  Receptor 1 is sufficiently distant from the source that



the entire  area is encompassed by a  22.5°  wedge directed  from  the  receptor



toward  the  source.   In  the  far-field  case,  the  virtual  point  source  is



displaced upwind of the  source center by a distance such that the  22.5° sector



width at the area source center matches the width of  the area source.



    In the second case.  Receptor 2 is  close  enough to the source  that a 22.5°



wedge  directed from  the  receptor  toward the source  does  not encompass  the



entire source.  In this case,  the source  emissions  are  reduced based  on  the



portion of the  square outside  of the 22.5° sector.  The upwind displacement of



the virtual  point  source in this case is  based on  the area source  width,



reduced to account for the area outside of the sector.



    In the third case, a  receptor  is  within the boundaries of  the area source



and  is only  affected  by the  upwind portion of the  source area.   The same



procedures used for case 2 are applied for this situation.








    2.3.11 Air Quality Dispersion Model



    The  Air  Quality  Dispersion  Model  (AQDM)  was  designed  primarily  for



estimating  long-term  averages of  S(>2 and particulates  in urban  areas.  AQDM



uses  a  virtual point  source  approach to  modeling area  sources.    The area



source  treatment in  AQDM  closely resembles  that  in  ISCLT.   AQDM  was  not



evaluated separately.








    2.3.12 Area Source Screening Techniques



    A  basic technique  to estimate short-term pollutant  concentrations due to



area sources is given in  the EPA document "A  Workbook  of Screening Techniques



for  Assessing Impacts  of Toxic Air  Pollutants"  (McNaughton  and Bodner, 1988)



which  is  based on the work  of  Turner (1970).   This  approach uses  the  virtual



point  source  method.  Area source width is used to calculate an approximate av






                                      2-18

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at the area source  using properties of  the Gaussian  distribution.   A  set  of



curves  is  provided  which   relates   ay  to   downwind  distance   for  each



Pasquill-Gifford stability  class.  The  distance thus  obtained is  termed the



virtual distance  (xv).   The virtual  distance  is  then  added to  the  actual



distance between the receptor  and the  area source.  This  combined  distance is



then used to obtain av and oz to  be used in the calculation of concentration.



The  concentration  is  calculated  in  a  straightforward manner using emission



rate,  wind  speed,   the plume  dispersion  parameters  and  source  height.   The



formulation avoids   integration   (or  the  numerical  equivalent)  making  it  a



"calculator friendly" method.



    Another screening  method for estimating concentrations  of pollutants from



an area source  is given by New York DEC,  Division of Air Resources.   In this



treatment, an  average  per  area  emission rate and  coefficient based  on area



size are used to calculate  concentrations within the area source.   A table of



concentration  reduction  factors   versus  distance  is  presented.   As  a first



approximation,  this  technique  is  valid for areas with  sides greater  than 350



feet in length  and  for -distances closer  than  3S of the source, where S is the



source side length.



    These  techniques   are   intended    to  provide   preliminary,   worst-case



concentration  estimates  to help  the  modeler  decide  whether  more  detailed



analysis  is  necessary.   Screening  techniques  are  not  designed   to  account



accurately for source-receptor geometry and were not evaluated in this study.








    2.3.13 Toxic Release Models



    Chemical spill  or accident models are of interest primarily because of the



chemical  libraries   included  in  the  codes and the  provisions  for treating



evaporation,  heavier-tnan-air gases, or other  specialized  problems.   The spill



models  generally contain  area  source  algorithms because  when  a  liquid  is






                                     2-19

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released, it  forms  a pool which evaporates  and acts as an area  source.   Many



of these models have relatively sophisticated algorithms to deal  with chemical



considerations, but most use very simple area source algorithms.



    The simplest  (very  conservative)  approach  to calculating  dispersion from



an area  source is  to  use the  area of a  pool to  calculate  total evaporation



from the pool and then to associate the amount evaporated with a point source



at the  center of the  pool.  This  approach obviously  is  not  adequate  in the



near-field in  the case  of extensive pools.  The  Areal  Locations  of  Hazardous



Atmospheres Model  (ALOHA) is a spill model  developed  by NOAA which provides



graphical output of dispersion plumes  due to chemical spills.   ALOHA  uses this



point approximation of the pool source as described.



    The  model entitled  "A Portable  Computing  System  for Use  in  Toxic  Gas



Emergencies  by the  Ontario Ministry of  the Environment"  (known as  the OME



Toxic Release Model) has an extensive chemical  library but  also treats  the



pool of spilled chemicals as a point source.



    The  Air  Force   Toxic Chemical  Dispersion  Model  (AFTOX)  is a  Gaussian



puff/plume  dispersion  model  which  was  developed  to model  emissions  from



chemical  spills.   AFTOX  is  a  coupled  emissions/dispersion  model and  uses an



inventory   of  chemicals  and  corresponding   chemical properties   to  model



dispersion from instantaneous or continuous leaks or  spills of  any chemical in



the  inventory.   The  emission algorithm  predicts  whether  a given spilling



chemical  will be in gaseous or  liquid form based on  an ambient temperature



input.   The  area of the  source is  then calculated  if the chemical is in  liquid



form, based on the  spill  rate.   A source strength  is then calculated based on



an evaporation algorithm.  The dispersion algorithm  itself is  non-reactive and



no decay or  deposition occurs.  It  is a  Gaussian  plume  model  under  steady



state,  non-stable conditions and it  is a Gaussian  puff model otherwise.  AFTOX



uses a  standard virtual point source  algorithm to simulate an area source.






                                      2-20

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    The  Shell  Development  Company   Evaporation/Air  Dispersion   Model   for

Chemical Spills on Land  (SPILLS)  is  another model to  simulate  the properties

of an  accidental  chemical release.   This model  also uses  the  virtual  point

source method  to  model area sources.  An initial ov is assumed based on area

source  width  and characteristics  of  the  Gaussian distribution, a virtual

distance is then calculated.

    None of these  models contains  an  area source  algorithm which  warrants

evaluation for the present study.



    2.3.14 Other Existing Models

    A number of other existing models were identified which contain algorithms

for  treating  distributed  (area and  volume)  emission  sources.   These models

either  were  designed for  applications  distinctly  different  from  the  air

quality  issues pertaining  to  Superfund/contaminated sites,  or  contain  area

source  algorithms which  were  judged to  be  redundant with algorithms  from

models  already chosen  for evaluation.   Listed  below  are three  other models

which were  reviewed:


    •  Mesopuff   -   Designed   for   modeling  long-range  transport.   Not
       appropriate for the time and distance scales of interest.

    •  Photochemical   Box  Model   -  Designed  for   modeling   reactive
       photochemical  pollutants.  Employs a  gridded  box  model equivalent
       to   volume  source.    Not  appropriate  for  near-ground  emission
       sources or near-field concentration estimates.

    •  APRAC1A -  Designed for modeling gridded traffic emissions based on
       line-source  algorithm.   Not  appropriate  for a single,  isolated
       area source.



2.4 Overview of Area  Source Dispersion Literature

    A  search of  recent technical literature identified few published articles

representing  unique  methods  for  estimating concentrations  downwind  of  area
                                      2-21

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sources.   Two published  articles were  identified which discussed  alternative



approaches to modeling area sources.   Neither approach represents  a computer



algorithm suitable for evaluation in the present study.



    The models suggested by  Hwang (1987) and Chitgopekar et al. (1988) attempt



to  resolve  some  of the  known  shortcomings  of  the currently  utilized  area



source  dispersion  algorithms.   The  discussion  by  Hwang  is theoretical  but



examines both a Gaussian approach and one based on  transport  equations in the



atmospheric  boundary layer.   Chitgopekar et  al. (1988)  attempts  to resolve



problems of near-field prediction through the use of  a "top-hat" formulation.



    Chitgopekar  et  al.  (1988)  presents  an  area   source  model developed in



response  to  problems with  virtual  point  models in the  near-field  for  area



sources.  These  authors  state  that "the most  rigorous" treatment  of Gaussian



dispersion from  area sources  would be  to model  them  as a  dense  matrix of



multiple  point sources.  This  idea  can be conceptualized  as increasing matrix



density until, in the  limit, inter-point spacing goes to  zero and  every point



in  the area is  emitting.   This  approach is computationally  intensive and is



rarely used in the  standard  models.  (The finite line segment approach in FDM



or  PAL  is  mathematically  equivalent  but  far  more efficient,  if Gaussian



dispersion is assumed.)  Virtual point source methods do not require  extensive



computations and can be  simplified to allow  manual  calculation.  However, the



virtual point source method should  not be used if the source width  is greater



than  40*  of the distance  between the  source  centerpoint  and the  receptor



(Hwang,  1986 as cited  in  Chitgopekar  et  al.,  1988).   This  is   a serious



limitation  due  to  the fact that the region  of  interest  in  many  area source



pollutant dispersion and exposure situations is in the near-field.



    The  treatment  by  Chitgopekar et  al.  is to  divide  the  dispersion of the



plume  into high  frequency  and  low frequency components.   A  low pass  filter is



used  so  plume  meander  is included  but small  turbulent  scales are not. The






                                      2-22

-------
model uses  a  multiple  line  source approach.   By eliminating  high frequency



fluctuations,  the  distribution  immediately  downwind  of   the  area  source



approximates a  top hat, with a  region of uniform concentration in the across



wind  direction,  downwind  of the  source.   This  treatment   is  equivalent  to



modeling the near-field region  downwind of the  source  as  if the source length



was  infinite.  Edge  effects  are eliminated by filtering  of   the  smaller (high



frequency)  fluctuations.   These authors  have developed this model so that it



can  be used with Pasquill-Gifford stability classes to  indicate plume meander



or   more   sophisticated  boundary  layer  parameters   obtained  through  eddy



diffusion (diffusion analogues  or similarity) equations.  The  authors believe



that  the  "top  hat" distribution  of  concentration in  the near-field  is  more



realistic that the Gaussian  plume shape predicted by the  virtual  point source



method.



    Hwang (1987) gives a method for estimating on-site  concentrations at toxic



waste  disposal  sites.   Gaussian dispersion models  using  the virtual point



method perform poorly in  this  type of near-field application.   Hwang states



that  when   using  a  virtual  point  approximation  for  an  area  source  of



pollutants, the receptor should be at least the  distance from source center to



the  property line or 100 m whichever is greater, downwind of  the source center.



    Hwang suggests  two models  for near-field  (on-site)  dispersion modeling.



The  first is a mathematical formulation of the multiple point source idea.  By



treating  the  area  as  being  composed  of  differential  point  sources,  the



concentration  contributions  to  an on-site  receptor  from  each   differential



source area can be summed.  This approach utilizes  the standard  point source



Gaussian concentration equations.



     The  second method  utilizes  a partial   differential  equation describing



atmospheric  dispersion.   This  formulation  utilizes  wind  speed  and transfer



coefficients   to  yield   concentration.    The    transfer   coefficients   are






                                     2-23

-------
parameterized  using the  standard  deviations  of  pollutant  spreading.   The

equation can then be solved with the appropriate boundary conditions.

    Hwang compares  the results  of  these  formulations  with results  obtained

using  a simple box  method.   The two  formulations given  in this  paper agree

quite  closely.   The box  model  yields  values  of concentration  greater by  a

factor of two than either of the methods proposed by Hwang.

    Hwang and Chitgopekar have demonstrated the advantages  of  alternative area

source  methods  compared to either  the  virtual point source approach  or a box

model.  Neither  author, however, discussed the  finite  line segment  approach

which  is  widely  used in  short-term models.   The  proposed methods appear to

offer  few advantages  when compared  to  the multiple  line  segment  approach of

FDM or PAL.



2.5 Limitations of Existing Models

    Gaussian  dispersion models  have inherent  limitations which  are  accepted

for  reasons  of  convenience and simplicity.  The  errors  induced by simplifying

Gaussian  assumptions  may be  compounded  by  the treatment  of  area  sources.

General problems with many Gaussian plume models are:


     1) The  effects  of atmospheric turbulence are not  well represented by
       a  Gaussian   plume  distribution.    To  represent   dispersion   of
       pollutants  by  atmospheric  turbulence  by  a  Gaussian distribution
        introduces error.

     2) Characterization of  the  mixing layer as a closed surface layer may
        introduce  error.   Escape of  pollutants at  the  top of  the mixing
        layer  and settling/deposition at  the surface  should both be loss
        terms  in  a  correct  formulation.   Some  of  the   models   include
        simplified treatments of settling/deposition.

     3) Many  of  the  dispersion  models  have  optional  decay  formulations
        which  allow  a  constant  chemical  half-life  to  be  entered.  Many
        VOCs  have chemical  reaction rates which  vary  substantially as a
        function  of  solar radiation, ambient  temperature,  humidity,  and
        the presence of other chemical pollutants.

     4)  Most   of  the   dispersion  models  do  not  treat  low  wind speed
        scenarios  adequately.   Zero wind speed  values  cause computational

                                      2-24

-------
       failure  and  very  small  values  may  cause  unrealistically  high
       concentration predictions.   This problem  is  typically  avoided by
       arbitrarily setting all wind speeds less than 1 m/s to be  equal to
       1 m/s.
    The  limitations  noted  above  and  uncertainties  relating  to  emissions

estimates  as described  in  Section 1.2  contribute  to  a  number of  specific

problems which  might  be anticipated  based on  the  source characteristics  of

contaminated sites and similar area sources.


    1) Since  many  area  sources  are  true  surface  sources,  low  wind
       velocities   will   occur   frequently  at   the   source   (from   a
       computational standpoint,  a "no-slip"  or  zero velocity  boundary
       condition may force velocity to be zero at the surface).

    2) Diffusion may  be  the  dominant  process  in  the  initial stages  of
       chemical  releases   from  many  surface  area  sources.    A  more
       sophisticated treatment of this process may be needed.

    3) Emissions from  many area  sources  may be  dominated by VOCs  which
       are  chemically  reactive  and have  half-lives  which are  strongly
       dependent on environmental conditions.


    The dispersion models  discussed here do not  contain emission models.   In

the  case  of manufacturing  processes,  it may be  reasonable  to model  with

constant  emissions.    For  the  types  of  pollutant  sources   discussed  here,

steady-state  emissions   will  not  be   a  reasonable   assumption.    Ambient

temperature, wind speed, moisture,  and site geometry  are  important  factors  in

determining source strength in many of these situations.
                                     2-25

-------
3.0 ANALYSIS OF MODEL PREDICTIONS FOR EXAMPLE APPLICATIONS

    The analysis of  model  predictions for  a set  of hypothetical  area source

test cases is designed to accomplish two  objectives:


    •  to characterize  the range  of  concentration  predictions  expected
       from each model, and the  differences between  models, as a function
       of   input  meteorology,   source   characteristics,  and   receptor
       locations.

    •  to examine whether model  predictions are consistent with  a  number
       of basic mathematical and physical principles.
3.1 Approach

    To  achieve  these  goals,  each  model  was  applied  to  a  standard  set  of

prediction  scenarios   which   represented  variations   on   a   "base   case"

source/receptor   configuration.    The  "base   case"   source  and   near-field

receptors are illustrated in Figure  3-1.   A single  square  area source  with

dimensions 150  m  x 150  m was  chosen.  The wind direction is parallel  to one

side  of  the  source.   Six  receptors  were  replaced  at  selected  distances

downwind  of  the area  source  center  ("centerline"  receptors).  The  receptor

distances are 100 m, 125 m,  175 m,  325 m,  875  m, and  1575 m downwind  of the

area source center.  (The  first four centerline receptors,  numbered R1-R4, are

shown in Figure  3-1.)   A second group of  receptors was placed downwind  of the

source edge.  These "edge" receptors are  numbered R7-R10.  In addition, model

predictions were  obtained  at  two  "on-source"  receptors,   Rll  and  R12,  as

shown.  Flat terrain was assumed.

    Concentration predictions  were  obtained  from each model  for  these  12

receptors, using  an emission  rate  of 1,000 g/s.   Three different  stability

conditions (Class B, D, and F)  were  modeled,  with a constant  wind  speed value

of 2  m/s and mixing height  of 5000  m.  For  Class D stability, both  a  ground
                                      3-1

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level source and a 10 m source height were  modeled.   For Class B  and Class F,



only  the  ground  level  source  was modeled.   Rural  Pasquill-Gifford  (P-G)



dispersion coefficients were selected for each model.








3.2 Tests of Mathematical and Physical Principles



    A series of  test  cases  representing variations  on  the "base  case"  source



geometry were  devised to examine whether concentration  predictions  from each



model  behave   in  a  manner   consistent  with   mathematical  and   physical



principles.   These  tests  are  designed primarily  to  examine  concentration



predictions for low-level emission sources  (height 10  m or less)  and for area



sources  ranging  from  50 x  50  m to  500 x  500  m.   The  primary focus  is  D



stability, which represents  the most common (but not the  worst-case)  stability



condition.  The following tests were applied:



    Stability  Comparison.   For  a ground  level source,  predicted  centerline



concentrations should  increase with  atmospheric  stability (Class  B  = lowest.



Class  F  =  highest).   The  influence  of  stability  class  should  be  more



pronounced at  larger  distances,  since horizontal dispersion near the source is



dominated by initial source  dimensions.



    Center  Versus  Edge   (Near-Field).   Immediately  downwind  of  the  source,



predicted concentrations at  edge receptors  should be  roughly one-half of  the



concentrations at  corresponding centerline receptors  when the  wind direction



is  parallel  to  the source  side.  For  a Gaussian  model,  the sources  which



produce  significant predicted  impact at  a given receptor are  those  located



upwind of the  source.   As the crosswind distance  between source  and receptor



increases, the  relative impact  decreases  according to the Gaussian equation.



A source at a crosswind distance of 3.03 ay produces one percent  of the  impact



of an identical  source located  directly upwind of  the  receptor.  The  source



region which  lies within ±3  
-------
the predicted impact.   At near-field distances, the "zone of  influence" within

±3 ay upwind  of  the centerline receptor is entirely filled by the area source,

as  shown in  Figure 3-2(a),  while  the  corresponding zone  upwind of  the edge

receptor is half  empty.   The empty and filled portions  of  the  influence zone

for the  edge receptor  are  mirror images.  The predicted concentration should

therefore be  one-half  of the concentration predicted when  the entire  zone is

filled.

    Subdivision.  The base case scenario is divided into four  equal  parts with

the same total  emissions,  as shown in Figure 3-2(b).   No significant change in

predicted  concentrations should  result,   since the overall  configuration of

source(s) and receptors is identical.

    Far-Field Convergence.   At downwind distances which are  large  relative to

source  dimensions,  predicted  concentrations  should depend on total emissions

but not  on source size.  (At  downwind  distances  where  Oy  is  as large  as the

area source  width,  the  plume  from an  area source will  be equivalent  to the

plume   from   a   point  source  with  the   same  emission  rate.)   Centerline

concentrations at distances  out to 8 km  were compared for three  area sources

with identical  emissions:   150 m x 150 m  (base case), 75 m x 75 m, and 450 m  x

450 m.   At 8 km, oy  for 0  stability  is   roughly  450  m, so  model predictions

should be  similar for  all three sources.

    Source Orientation.   The orientation of the wind direction relative  to the

area   source  will  influence  predicted  concentrations  near   the  source.

Predictions   for  the   base  case  were   compared to  predictions  for  the

configuration shown in Figure 3-2(c),  with the wind direction at a  45°  angle

to the source  axes.   Centerline receptors  were  placed at the same  distances

downwind of  the  source's center.  Two results  are  expected:


     1.  Predicted centerline  concentrations   near  the  source  should be
        higher for the  "diagonal"  configuration,  because a larger source
        area  falls within the "zone  of influence".

                                      3-4

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    2. Farther from  the  source,  the  two configurations  should  produce
       similar  concentrations.    (See   the   discussion  of   "far-field
       convergence".)


    Source Height.   The  use  of  non-zero source  height should produce  lower

predicted  concentrations  in  the  near-source   region.   Differences  should

decrease  as  downwind  distance  increases.   Predictions for  a 150  m x  150  m

source  with  10 m height  were  compared  against  the  base  case.   At  875  m

downwind  distance,  oz  for  0   stability  is  roughly  28  m,  and  centerline

concentrations predicted  for a 10 m  source  height should be  about  94 percent

of predictions for a ground-level source.

    All  of  these  tests  are  well-suited  for  testing  short-term  (hourly)

models.   Some of  the tests  are more difficult  to  interpret  for  long-term

models,  which are  designed  to  predict  sector-average concentration  values

based on  wind direction  frequency.   The center/edge  comparison and  the  source

orientation test were only applied to the short-term models.



3.3 Predicted Concentrations for Base Case

    3.3.1 Short-Term Models

    Predicted  concentrations  for the  base case  source-receptor  configuration

were  obtained for three  stability  conditions (Class D, Class B,  and Class F)

at a  constant wind speed of 2 m/s.  The centerline concentrations  predicted by

the  five short-term  area  source models  are  illustrated in  Figures 3-3, 3-4,

and 3-5.  In  these figures,  the downwind distance  is measured from  the center

of the  area  source.   For all three stability conditions,  a similar pattern is

evident.   FDM consistently  predicts   the  lowest  concentration  values at all

distances.   SHORTZ generally predicts the highest concentrations  within 150 m

of  the source, while RAM predicts the  highest  concentrations  beyond 500  m.

PAL and ISCST predict  intermediate values at  all distances.
                                      3-8

-------
    At small downwind distances,  the  concentrations predicted by RWi ara lower



than  these  predicted by  PAL  and  SHORTZ.    (The   closest  distance,  37.5  m,



represents receptor Rll, located  within the area source.   See Figure 3-1.  The



ISCST model will  not  predict concentrations  inside of an area  source.)  The



gap  between predictions  by  RAM  and  the  other  four models  increases with



downwind distance for all  three  stability conditions.



    At  a  distance  of   100  m,  centerline  concentration predictions   for  D



stability (Figure 3-3) range from a low value of 2.6 x 10s ug/ra3 predicted by



FDM  to  a maximum value  of 7.5  x 10s  ug/m3  predicted by  SHORTZ.  At  1575  m



downwind, centerline predictions range from 1.3 x 10* ug/m3 (FDM)  to  6.2 x 104



ug/m3 (RAM).  Near  the  source,  at distances  between 100  m and  175 m,  PAL and



SHORTZ give nearly  identical centerline  predictions.   As  distance  increases,



SHORTZ predictions decrease faster than PAL's.  The ISCST  prediction at 100 m



is  roughly  a  factor of  2  lower than  PAL  or  SHORTZ,  but  ISCST  and  PAL



predictions converge as  distance increases.



    For  B  stability  (Figure 3-4),  SHORTZ  predicts  the  highest  centerline



concentration at 37.5  m   (on  the  source).   At  distances beyond 200  m,  RAM



predictions are  the highest  and  FDM predictions  are lowest.   Predictions by



PAL  and  SHORTZ are  very similar at all distances for B stability.  Predictions



by  ISCST are lower  in  the  near-field but  converge  with PAL  and  SHORTZ at



375 m.   At  1575  m,  the RAM  concentration is  a factor  of  4 higher  than all



other models.



    For  F  stability  (Figure 3-5),  SHORTZ  predicts  the  highest  centerline



concentrations at 38  m  and 100  m.  FDM predicts the lowest  concentrations at



all  distances.   SHORTZ  predictions span the  largest  range  for F  stability,



since SHORTZ predicts the highest  concentration at 37.5 m and matches FDM for



the  lowest  concentration at  1575  m.   PAL  predictions agree  with SHORTZ near
                                      3-9

-------
the  source,  while  ISCSI predictions  are  lower by  about a  factor of  2.   At



greater distances, however,  PAL, RAM and ISCSI predict similar values.



    These base case results  illustrate the effects of differences between area



source algorithms and in the models' treatment of dispersion.   The  RAM "narrow



plume  simplification"  apparently  causes  RAM predictions  to diverge  from  the



other  models  at  large  distances   for B  and  D  stability.    SHORTZ  and  PAL



generally produce  similar near-field  predictions,  which  indicates  that their



area source algorithms  are  similar  (from  an operational  perspective).   ISCST



and  PAL generally  agree at  875  m and  1575 m,  but  disagree closer  to  the



source,  indicating differences  in  the  area  source algorithms,  but  similar



dispersion  treatment.   FDM's  treatment  of  both the area  source  (near-field)



and dispersion coefficients is unique among this group of models.



    The  magnitude  of  centerline  concentrations predicted  by  the  short-term



models changes significantly with  source height.   Predicted concentrations for



a  10  m  source  height  for  D  stability  are  illustrated  in  Figure 3-6.   The



near-field  concentrations predicted by  RAM and PAL decrease  by  more  than a



factor of  10 when the source  height  changes from  zero  (Figure 3-3)  to  10 m



(Figure 3-6).  The  maximum  predicted concentration in Figure  3-6  is 1.9 x 10s



ug/m3,  for SHORTZ,  versus   1.8 x  10* ug/m3  for  SHORTZ  in  Figure 3-3.   The



relative rank of predictions by different models changes with source height at



distances  out  to 175 m.  For  example, FDM predicts higher concentrations than



PAL  or RAM for the  10 m source  height.








     3.3.2 Long-Tenn Models



     The  concentrations  predicted  by the  three  long-term   (sector  average)



models for the  base  case are  illustrated  in  Figures 3-7  through 3-10.  For a



ground-level  150 x 150  m source  and  D  stability  (Figure 3-7) ISCLT predicts



the  highest centerline  concentrations near the source, and  COM  predicts the






                                      3-10

-------
highest  values   beyond  200  m.    VALLEY  predicts   the   lowest  near-field



concentrations.   Beyond 500 m, ISCLT and VALLEY predict similar values.



    For  B   stability  (Figure 3-8),  CDM  predicts   the   highest  near-field



concentrations.   Beyond 500 m, CDM and VALLEY  predict similar  values.   ISCLT



predicts  higher  near-field  values  than  VALLEY,  but  predicts  the  lowest



concentrations beyond 300 m.



    For F  stability (Figure 3-9), VALLEY predicts  the highest concentrations,



CDM  predicts the  lowest near-field concentrations,  and   ISCLT  predicts  the



lowest concentrations beyond 800 m.



    For a 10 m source height and D stability (Figure 3-10),  ISCLT predicts the



highest concentrations  at  100 m and  125  m,  while  CDM predicts  the highest



values  beyond  200 m.   Predictions  for  these  long-term  models  were  less



sensitive to  the  change in source height than  predictions  from several of the



short-term models.








3.4 Tests of Mathematical and Physical Principles



    The  results obtained  for the tests  of physical principles  defined  in



Section 3.2.1 are described below for each area source model.








    3.4.1 ISCST



    Stability Comparison.   The  centerline concentrations  predicted  by ISCST



for  the base case configuration for Class B, D,  and F stability  are compared



in  Figure 3-11.   As  expected,  predictions  increase  as stability increases.



The  relative  difference  in  predicted  concentrations  also  increases  with



distance from the source.  Concentrations  for B  and F stability  differ by a



factor of 5 at 100 m and a factor of 30 at 1575 m.



    Near-Field  (Center  Versus Edge).  Predicted concentrations at receptors Rl



and  R7 are  identical.   Similar  results  were   obtained at  other  near-field






                                      3-11

-------
receptors.    (If   ISCSI   correctly  accounted   for  source   geometry,   the



concentration at R7 would be one-half the value predicted at Rl.)



    Subdivision.  Predicted centerline concentrations are  shown  in Figure 3-12



for the  single 150 m  x 150  m source and the subdivided source  (four  75  m x



75 m sources,  as shown  in Figure 3-2a).  At  100 m distance  (25  m from  the



downwind edge  of  the  source area), ISCSI  predicts concentrations  different by



a  factor  of  2  for  these  equivalent  source  configurations.   At  325  m,



concentrations  differ  by 25 percent.  Higher  concentrations are predicted for



the subdivided source.   These results are not physically reasonable.



    Source Orientation (Figure 3-13).   Centerline concentrations  predicted by



ISCST  are nearly identical  for normal  and diagonal  wind directions.   I SCSI



predictions  do not reflect the difference  in  source-receptor  geometry between



these two cases.



    Source Height.   Centerline concentrations  predicted by ISCST for a  10 m



source height are lower  than those predicted for a  ground  level  source,  as



shown  in Figure 3-14.   Differences  are  largest  near  the source,  exceeding a



factor of 2  at 100 m, and diminish as distance increases.  These results  are



physically reasonable and  consistent with P-G dispersion coefficients.



    Far-Field Convergence.  Centerline  concentrations predicted  by  ISCST  are



shown  in Figure 3-15  for  three area  sources  with equivalent  total emissions



but different areas  (75 x  75  m;  150 x 150 m; and 450  x  450  m).   Differences



are largest  near the source and diminish  as  distance  increases.  The smallest



area source  produces the highest predicted concentrations.  At 100 m distance,



the  centerline  value  for  the 75  x  75 m  source  is more than  5  times  the



corresponding value for  the 450 x 450 m source.  These  results are consistent



with source-receptor geometry  and Gaussian dispersion.



    Summary  - ISCST.   Results obtained for ISCST are  reasonable for the tests



based  on  sensitivity  to  stability class  and  source  height.  ISCST  did not






                                      3-12

-------
correctly   account   for   source-receptor  geometry  in   the   center/edge,



subdivision, and source orientation tests.








    3.4.2 FDM



    Stability Comparison (Figure 3-16).   Predicted  centerline  concentrations



increase with stability, as  expected.   At 100 m distance, concentrations for B



and F stability differ by only a factor of 2.   At 1575 m, the difference is a



factor of 20.



    Center Versus Edge.  Predicted concentrations at  receptors downwind of the



source  edge  (R7, R8)  are  one-half of  the corresponding values  at centerline



receptors.  This result is consistent with source-receptor geometry.



    Subdivision   (Figure 3-17).    Predicted   centerline   concentrations   are



approximately 20 percent  lower at  100  m distance  for the  subdivided source,



and 10  percent  lower beyond 500 m.  These differences represent  an inaccurate



treatment of source geometry.



    Source Orientation  (Figure 3-18).   Predicted centerline  concentrations are



higher  near the  source for  the  diagonal orientation,  consistent  with  the



source-receptor geometry.  Results converge at greater distances.



    Source Height (Figure 3-19).   Predicted centerline concentrations  at 100 m



from  the  source are  a factor of  2 lower for the 10  m  source  height than a



ground  level release.   At  325 m and 875  m,  the  differences are  smaller than



values estimated using P-G dispersion coefficients.



    Far-Field Convergence  (Figure 3-20).   In  the near-field  region,  predicted



centerline  concentrations  decrease as  source  area  increases.    As  distance



increases, results for all three source sizes  converge.



    Summary - FDM.   FDM provides physically reasonable results for all but one



of the  tests,  indicating  that  the treatment  of  source-receptor  geometry  is



generally  accurate.   The  subdivision  test   results  suggest  some  room  for



improvement.




                                     3-13

-------
    3.4.3 PAL



    Stability  Comparison  (Figure 3-21).   Canterline  concentrations  increase



with increasing stability.   Differences become  larger as  distance  increases,



growing from a factor of 5 at 100 m to a factor of 50 at 1575 m.



    Center  Versus  Edge   (Near-Field).    Predicted  concentrations   at  edge



receptors near  the  source  (R7,  R8) are one-half  the  corresponding centerline



concentrations.  This result is consistent with source-receptor geometry.



    Subdivision  (Figure 3-22).   Predicted centerline  concentrations for  the



subdivided  source  are  almost  exactly  equal  to  the  corresponding  values



predicted  for  the   single  source  at  all  distances.   These  results  are



physically reasonable.



    Source Orientation  (Figure 3-23).   Predicted centerline concentrations for



the diagonal  source  orientation  are higher than the values for a  normal wind



direction.  Differences become insignificant as distance increases.



    Source Height  (Figure 3-24).  Predictions  in the  near-source  region  are



very   sensitive  to  source   height.    With  a   10   m   release  height,  the



concentration at  100  m is lower by  a factor of  10.   Predictions  converge as



distance increases.



    Far-Field  Convergence  (Figure 3-25).   Predictions  for three  area   source



sizes  converge at 8000  m from the  source.   In the near-field, concentrations



decrease  as  source  size  increases.   These  are  the  expected  results   for  a



properly functioning model.




    Summary  - PAL.   The  results for PAL  are physically  reasonable for  all



tests.  Predictions are very sensitive to source height.








    3.4.4 RAM



    Stability   Comparison   (Figure  3-26).    Centerline  predictions   by  RAM



increase  with stability.   The difference between  B and  F  stability  increases



from a factor  of  4 at  100 m to a factor of 10 at 1575  m.



                                      3-14

-------
    Center Versus  Edge  (Near-Field).   RAM  predicts  identical  concentrations



for centerline receptors  (Rl,  R2)  and for edge  receptors  (R7,  R8).   For this



test, RAM did not correctly account for source-receptor geometry.



    Subdivision  (Figure  3-27).   RAM  correctly  predicts  identical  centerline



concentrations for the single  source  150 x 150  m  base case and the subdivided



(identical) source.



    Source   Orientation    (Figure  3-28).    RAM  predicts   higher   centerline



concentrations for the diagonal  wind  direction than for the  normal direction.



The results are  reasonable  near  the source.  At larger distances,  predictions



are 25  percent higher for  the diagonal  orientation.   These  results are  not



consistent with conservation of mass.•



    Source Height (Figure 3-29).   RAM predictions  are extremely sensitive  to



source height.   Centerline  predictions  for  10 m source height  are lower than



predictions for a ground level source  by a factor of 10 at  100  m.   Predictions



for these two cases  converge as distance increases.



    Far-Field Convergence (Figure  3-30).  RAM  predictions  for  three different



area source sizes do  not show convergence even at distances beyond 2000 m.  In



fact, the relative differences remain  constant between 800  m and 8000 m.



    Summary -  RAM.  RAM  predictions are not reasonable  for a single, isolated



area source.   (The  "narrow  plume hypothesis" employed  by  RAM is designed for



application  to  a  large  area source  grid, and assumes  that   emissions vary



slowly  between  adjacent grid  squares.)   RAM  does  not   produce  physically



reasonable  results  for  the  center-edge,  source  orientation,  and far-field



convergence tests.








    3.4.5 SHORTZ




    Stability  Comparison (Figure 3-31).   Predicted centerline  concentrations



increase with  stability  at  all distances.  The  difference  between  Class B and






                                     3-15

-------
Class F predictions  is  a factor of 5 at  100  m but increases to a factor of 20



at 1575 m.  These results are physically reasonable.



    Center/Edge Comparison.   Near-field concentrations predicted by SHORTZ at



"edge"  receptors  (R7.R8)   are   one-half  of  the   corresponding  centerline



predictions, consistent with the source-receptor geometry.



    Subdivision  (Figure  3-32).   Predicted  centerline  concentrations  for  the



150 x  150 m source and the  equivalent  four 75 x 75 m sources are identical at



all  off-source  receptors.    For  the  on-source   receptor  Rll,   a  higher



concentration  is predicted  for  the subdivided  source.  The  results indicate



that source geometry is correctly accounted for.



    Source  Orientation  (Figure  3-33).   SHORTZ  predicts identical  centerline



concentrations  for  diagonal and  normal  wind  directions.    SHORTZ  does  not



account   for  the   different  source-receptor  geometry  and   its  effect  on



near-field concentrations.



    Source Height  (Figure 3-34).   Predicted centerline concentrations near the



source are lower for the 10  m source  height than for  the ground level source,



as  expected.   Differences of  20 and 10  percent remain  at  875  m and 1575 m,



respectively.  These differences,  while small, are larger than expected based



upon the Pasquill-Gifford vertical dispersion coefficients for D  stability.



    Far-Field  Convergence  (Figure 3-35).    Predicted  centerline  concentrations



within 1000 m of  the  source are significantly different for  the three source



sizes  (75 x 75,  150 x  150, 450 x 450 m).   The  larger source  areas produce



lower  predictions.   Centerline  predictions  converge  at   greater  distances.



These  results  are physically reasonable.



    Summary  -  SHORTZ.   Results indicate that  the  SHORTZ area source  algorithm



predicts  reasonable  concentration patterns for most  scenarios,  but  does  not



always respond to details of source-receptor geometry.
                                      3-16

-------
    3.4.6 ISCLT



    Stability Comparison  (Figure 3-36).   Predicted centerline  concentrations



for B,  0,  and F  stability increase  with increasing  stability.   Differences



between B and F  stability are a factor of 5  at 100 m  and a  factor of  10 at



1500 m.



    Subdivision (Figure 3-37).  ISCLT  predicted higher  concentrations  for the



subdivided source area  (four  75 x 75 m sources), compared to the single (150 x



150 m) source.   The difference  is approximately a factor of 1.5 at 100 m, and



a small difference (10 percent) remains at 1575 m.



    Source Height  Comparison   (Figure 3-38).   Centerline  predictions by  ISCLT



are not  strongly affected  by source height.   Differences  between predictions



for a  10  m  source height versus a  ground level source are less than a factor



of 2 at 100 m and decrease as  distance increases.



    Far-Field Convergence  (Figure 3-39).   Predictions  for  the 75  x 75  m and



150 x 150  m area  sources converge gradually  with increasing distance.   At



8000 m, the  centerline concentration  for these  sources  is 20 percent higher



than the corresponding value for the 450 x 450 m source.



    Summary  -  ISCLT.   The  area source predictions  by  ISCLT  show differences



for the  subdivision and  far-field  convergence  test which are not consistent



with source geometry and Gaussian plume dispersion behavior.








    3.4.7 COM



    Stability Comparison  (Figure 3-40).   Centerline concentration predictions



by  COM increase  with increasing stability.   The difference  between B  and F



stability is less than a  factor of  2 at  100  m and increases to a  factor of 6



at 1500 nr.




    Subdivision  (Figure 3-41).   CDM's predictions for  the subdivided  source



are indistinguishable from the base case concentrations at all distances.






                                     3-17

-------
    Source Height  (Figure  3-42).   CDM predictions  are not  strongly sensitive



to  source height.   The  difference  between  predictions  for  150  x  150  m



ground-level and 10 m  sources  is only 25 percent at 100 m.  Using P-G vertical



dispersion  coefficients  (CTZ)   for  D  stability,  a   larger   difference  was



estimated at 100 m, but a much smaller difference was estimated at 1500 m.



    Far-Field    Convergence    (Figure 3-43).     The    differences    between



concentrations predicted for three  area  source sizes  are relatively  small at



near-field distances.  The  predictions appear to converge at 3km downwind, but



large differences are predicted by CDM at 8  km.   For the 75 x  75m source, the



predicted concentration at 8km is higher than at 3km.



    Summary  -   CDM.    CDM  predictions   for  the  stability   comparison  and



subdivision  tests  are  physically  reasonable.   For  the  source height  and



far-field  convergence  tests,   CDM  predictions  were  not   consistent  with



source-receptor  geometry and Gaussian plume dispersion behavior.  Inaccuracies



may be  related  to the spatial  resolution of the array used by  CDM for upwind



integration.








    3.4.8 VALLEY



    Stability  Comparison  (Figure 3-44).   Predicted  centerline  concentrations



for  F stability are  distinctly different  from,  and much higher  than, those



predicted for  B and D stability.   Near  the source, concentrations for  B and D



stability  are  very similar,  and  differences  increase  with  distance.   The



near-field  concentrations  for  F stability are greater by more than a factor of



10.   No  physical basis for  these differences is apparent.



    Subdivision  (Figure 3-45).  Predicted   centerline  concentrations increase



when  the base  case source is  subdivided.   VALLEY predicts concentrations 50



percent  higher at  100 m and 5 percent higher at 1500 m.
                                      3-18

-------
    Source  Height  (Figure  3-46).   Predicted  concentrations  for an  elevated



area source  are only  5 percent  lower  than  corresponding  predictions  for  a



ground  level  source.   Much  larger  differences were estimated  at distances of



100-200 m using the P-G dispersion coefficients.  Larger  differences  were also



predicted by the other models.



    Far-Field Convergence  (Figure 3-47).  Concentrations  predicted by VALLEY



for three area  source  sizes converge gradually at  distances beyond 800 m from



the source.   At a  distance of 8000  m,  the  predicted  concentration  for  the



450 x 450 m  source  is  20   percent  lower  than values  for the 75  x  75  m  and



150 x 150 m sources.



    Summary  - VALLEY.   The  differences in concentrations  predicted  by VALLEY



as  a  function of  stability,  source  geometry  and  source  height  are  not



consistent with predictions by other models and do not correspond to Gaussian



plume  dispersion  behavior.   Inaccuracy  of  20  to  50  percent  was  found  in



several cases.








3.5 Discussion of Results



    Predicted  concentrations were compared and analyzed  for eight  air quality



models applicable to area source emissions.  The model  scenarios  used for this



analysis  represent  variations  on  a base  case  with a single 150 x 150 m area



source  and  receptors  extending  out  to  1575  m  from  the  source.   Model



predictions   were   analyzed  to  characterize  the  range  of  concentrations



predicted as a function of stability class and source  height.   Test  cases were



designed  to  identify  changes  in  predicted  concentrations   associated  with



specific  changes  in   the   source-receptor  configuration.   These  tests  were



analyzed  to identify  strengths and weaknesses in  each area source algorithm,



independent of each model's treatment of atmospheric dispersion.
                                      3-19

-------
    3.5.1 Short-Term Models



    Five short-term models  (ISCSI,  FDM,  PAL, RAM, SHORTZ) were analyzed in the



greatest   depth.     In  general,   FDM   predicted   the   lowest   centerline



concentrations.  Within 200 m of  the  source,  SHORTZ  predicted the  highest



concentrations.  Beyond 400 m, RAM predicted the  highest  values.    (The  low



concentrations  predicted   by   FDM   result   primarily   from   differences  in



dispersion rates, not from the  area source algorithm.)



    The  tests of  physical principles provide a basis  for judging  each area



source  algorithm's  strengths and weaknesses based  on  "absolute"  performance



criteria,  independent   of   any  measured  concentrations.   The  results  are



summarized in Table 3-1 and are briefly discussed below:



    Stability  Comparison.    All of the  models'  predictions  agreed with the



expected changes in concentrations with stability class and downwind distance.



    Source   Height.    All  of  the   models   predicted   lower  near-source



concentrations when  the source height was  increased from  zero to 10  m.   For



SHORTZ, however, predictions did not converge at a distance of 1575 m.



    Far-Field  Convergence.   Predictions  were  obtained for three  area sources



with  different dimensions  but  the same total emissions.  For all of the models



except  RAM,   predicted concentrations were  independent  of source  size  at  a



distance of 8000 m.



    Center/Edge.   Near the  source,  predicted concentrations  downwind  of the



area  source  edge  should be one-half of  centerline  concentrations.   FDM, PAL,



and  SHORTZ correctly predicted  this behavior.   RAM and  ISCST  predicted no



difference in concentration between centerline and edge receptors.



    Subdivision.   The base  case  area source  was subdivided  into  four equal



parts,  representing  (in total)  the identical  source.   PAL,  RAM  and SHORTZ



correctly  predicted identical  concentrations.   FDM  predictions  were different



by 10 to 20 percent.   ISCST predictions  for the subdivided source were higher



by a  factor of 2.



                                      3-20

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    Source  Orientation.    Centerline  concentrations  were  compared  for  wind



directions along  a  source side  and along  the diagonal.  Near-source  impacts



should  be  higher  for  the   diagonal  direction,  but  differences  should  be



negligible  at greater  distances.   ISCST  and  PAL  predicted  this  behavior



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    Overall,  the   FDM   and  PAL   algorithms  consistently   gave   physically



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    3.5.2 Sector Average Models



    Three  sector  average  models  were  examined:    ISCLT,  CDM,  and  VALLEY.



Results  for  all  three models  indicate  that source  integration methods are



subject  to inaccuracies of  10  or 20 percent.  Aside from these inaccuracies,



the ISCLT algorithm gave reasonable results  based on  the stability  comparison,



source  height,  and  far-field  convergence  tests.   ISCLT  failed  to  account



properly  for  source  geometry,  however, based  on the  subdivision  test.   CDM



gave  reasonable  results  for the  subdivision,  stability,  and  source  height



comparisons,  but the far-field  predictions at 8km  were not  reasonable.   The



VALLEY  algorithm gave physically unreasonable results based upon the stability



comparison, the subdivision test, and its lack of sensitivity to source height.
                                      3-22

-------
                              Rgure  3—3
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                         Model: SHORT TERM GROUP
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                         Total Source Strength: 1  kg/s
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                          3-23

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                              3-24

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                  Rgure  3—7
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                               COM
   500       1000      1500       2000
Downwind  Distance  (m)
             3-27

-------
                              Figure 3—8
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                       3-28

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                             3-30

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                                Test STA8IUTY COMPARISON
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                                Source Height Om
                                Wind Speed: 2 m/s
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                                Total Source Strength: 1 kg/s
                                 STABILITY F
                                                   STABILITY D
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   500       1000       1500
Downwind  Distance   (m)
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                               3-31

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                         Wind Direction: 360
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                                                   4<75m X 75m) (»)

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                                3-32

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                              3-33

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Stability: D
Source  Size: 75m X 75m. 150m X  150m. 450m x 450m
Source  Height: Om
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Total Source Strength: 1 kg/e
                                                75m  X 75m  (*)

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                                      Figure 3—16
                                Model: FDM
                                Test: STABILITY COMPARISON
                                Stability: D. B. F
                                Source Size: 150m X 150m
                                Source Height Om
                                Wind Speed: 2 m/s
                                Wind Direction: 360
                                Total Source Strength: 1  kg/s
                                                    STABILITY F
                                                    STABILITY D
                                                    STABILITY B
                      500        1000       1500
                   Downwind  Distance  (m)
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                               3-36

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                              Figure  3—17
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                       T«t: SUBDIVISION COMPARISON
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                       Total Source Strength: 1 kg/e
                         150m X 150m (o)
                                         4(75nT\ 75m) (*)
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   500       1000      1500
Downwind  Distance (m)
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                             3-37

-------
10 *-i
                               Rgure 3—18
                         Modal: FDM
                         Test SOURCE ORIENTATION
                         Stability: D
                         Source Size: 150m X 150m
                         Source Height: Om
                         Wind Speed: 2 m/s
                         Wind Direction: 360, 315
                         Total Source Strength: 1  kg/3
                                             315 (o)

                                             360 («)
               500        1000      1500       2000
             Downwind   Distance  (m)
                       3-38

-------
      10'I
                                   Rgure  3—19
 " JMo ••
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                             Model:
                             Twt SOURCE HDGHT COMPARISON
                             Stability: D
                             Source Sin: 150m X 150m
                             Source Height: Om, 10m
                             Wind Speed: 2 m/8
                             Wind Direction: 360
                             Total Source Strength: 1 ka/e
                                                Om (*)
                                               10m (o)
                    500       1000      1500      2000
                 Downwind  Distance  (m)
                             3-39

-------
      10 'q
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      102-
                Model: FDM
                Teefc FAR-FIELD CONVERGENCE
                Stability: D
                Source Size: 75m X 75m. 150m X 150m. 450m X 450m
                Source Height Om
                Wind Speed: 2 m/s
                Wind Direction: 360
                Total Source Strength: 1 kg/a
                                              75m X 75m (o)
                                              150m X 150m (x)
                                              450m X 450m (*)
                  •      ••••tt
                 1     234567

                  Downwind  Distance  (km)
                                                     8
                                3-40

-------
   10 7q
   10
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-------
                                 Figure 3-22
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                         Model: PAL
                         Tttt: SUBDIVISION COMPARISON
                         Stability: 0
                         Sourc* Sfe«: 150m X 150m. 4<75rn X 75m)
                         Source Height: Om
                         Wind Speed: 2 m/9
                         Wind Direction: 360
                         Total Source Strength: 1 kg/e
                                                150m X 150m (*)
                                                4<75m X 75m) (o)
                     500        1000       1500

                   Downwind  Distance  (m)
                                                     2000
                               3-42

-------
      107T3
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-------
      10 7
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                                    Figure 3-24
                              Modefc PAL
                              Test- SOURCE HQGHT COMPARISON
                              Stability: 0
                              Source Sire: 150m X 150m
                              Source Height: Om, 10m
                              Wind Speed: 2 m/s
                              Wind Direction: 360
                              Totd Source Strength: 1 kg/e
 Om (*)

10m (o)
                     500       1000       1500      2000
                  Downwind   Distance  (m)
                            3-44

-------
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                              Rgure 3-25
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                Source Siz«: 75m X 75n>. 150m X
                Source Height* Om
                Wind Speed! 2 rn/8
                Wind DirBCuoni 960
                Total Source Strength: 1  kg/s
                                         150m. 450m X 450m
                                                    75m X 75m (*)
                                                    150m X 150m (o)
                                                    450m X 450m (x)
                 I      f      •      •      I      I      I
                 1      234567

                 Downwind  Distance  (km)
                                                          8
                                  3-45

-------
                                   Rgure  3—26
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                              Mod*fc RAM
                              T«st STA8UJTY COMPARISON
                              Stability: D, B, F
                              Source StiaK 150m X 150m
                              Sourco H
-------
      10'-]
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                               Figure  3—27
                        Model: RAM
                        •tot- SUBOIMSION COMPARISON
                        Stability: 0
                        Soun* Ste«: 150m X 150m. 4(75rn X 75m)
                        Source Heiahb Om
                        Wind Speed: 2 m/s
                        Wind Direction: 360
                        Total Source Strength: 1 ko/i
      150m X 150m (*)


      4<75m X 75m)(o)
500
1000
1500
                                                      2000
                  Downwind  Distance  (m)
                             3-47

-------
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     Test: FAR-FIELD CONVERGENCE
     Stability: O
     Source Size: 75m X 75m. 150m X 150m. 450m X 450m
     Source Height- Om
     Wind Speed: 2 m/s
     Wind Direction: 360
     Total Source Strength: 1 kg/8
                                                      75m X 75m (*)
                                          150m X 150m (o)
                                          450m X 450m (x)
   •  I  i   I  i   I   '   I   i   I   i   I   i   I   i  i
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       Downwind  Distance  (km)
                                  3-50

-------
     10 7
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-------
10 7^
                          Figure  3—32
                  Modal: SHORTZ
                  Teefc SUBDIVISION COMPARISON
                  Stability: D
                  Source Size: 150m X 150m. 4(75m X 75m)
                  Source Height: Om
                  Wind Speed: 2 m/s
                  Wind Direction: 360
                  Total Source Strength: 1 kg/e
                                            150m X 150m (*)

                                            4(75m X 75m) (o)
10
     0
   500       1000       1500      2000
Downwind  Distance  (m)
                           3-52

-------
10 7q
                              Rgure 3—33
                        Ifcxtek SHORTZ
                        T«at SOURCE ORIENTATION
                        Stability: 0
                        Source Sa«: 150m X 150m
                        Source Height Orn
                        Wind 5p60d! 2 m/8
                        Wind Direction: 360, 315
                        Total Source Strength: 1 tog/»
   5_
                                           360 (*)

                                              (o)
104-
               500       1000       1500      2000
            Downwind   Distance  (m)
                      3-53

-------
      10 7i
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-------
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                              Rgure 3—35
                Mod*: SHOR1Z
                T«8t FAR-HELD CONVERGENCE
                Stability: D
                Source Siz«: 75m X 75m. 150m X 150m. 450m X 450m
                Source Height: Om
                Wind Speed: 2 m/s
                Wind Direction: 360
                Total Source Strength: 1  kg/e
                                                     75m X 75m (*)
                                                     150m X 150m (o)
                                                     450m X 450m (x)
                 I   '   I   •   I   '   I
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                                                    8
                 Downwind   Distance   (km)
                            3-55

-------
      10 *q
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                                     Rgure 3—36
                                Model: SCLT
                                Test STABJUTY COMPARISON
                                Stability: 0. B, F
                                Source Size: 150m X 150m
                                Source Height Om
                                Wind Speed: 2 rn/a
                                Wind Direction: 360
                                Total Source Strength: 1 kg/s
                                                   STABILITY F
                                                STABILITY D
                                                STABILITY B
           0
                  500       1000      1500      2000
               Downwind   Distance   (m)
                                 3-56

-------
     10
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                              Rgure  3-37
                       Model: SCLT
                       Twt- SUBDIVISION COMPARSON
                       StobHRy: 0
                       Souro Sfe«: 150m X 150m. 4(75m X 75m)
                       Source Height Om
                       Wind Speed: 2 m/»
                       Wind Wriction: 360
                       Total Source Strength: 1 kg/e
4(75m X 75m)(o)

1SOm X 150m (*)
                   500       1000      1500      2000
                 Downwind  Distance  (m)
                           3-57

-------
     10 f-i
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                                   Figure  3—38
                             Mod»k
                             T«»t SOURCE HBGHT COMPARISON
                             StabJBty: D
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                             Source Height Om, 10m
                             Wind Speed! 2 m/s
                             Wind Direction: 360
                             Total Source Strength: 1 kg/»
                                                Om (*)

                                               10m (o)
         III
                  I  I  I  I
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   500       1000      1500      2000
Downwind  Distance  (m)
                            3-58

-------
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                StobUtty: 0
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                Source Height Om
                Wind Speed: 2 tn/9
                Wind Direction: 360
                Total Source Strength: 1 kg/e
                                         150m, 450m X 450m
                                                    75n» X 75m (x)

                                                    150m X 150m (o)
                                                    450m X 450m (*)
                                                     i
                 1     23456
                  Downwind  Distance  (km)
                                                          8
                                  3-59

-------
     10 "q
                                  Figure 3—40
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                             Model: COM
                             Test STABIUTY COMPARISON
                             Stability: D, B, F
                             Source Size: 150m X 150m
                             Source Height: Om
                             Wind Speed: 2 m/s
                             Wind Direction: 360
                             Total Source Strength: 1 kq/s
STABIUTY F


STABIUTY D
STABIUTY B
                    500       1000      1500      2000
                 Downwind  Distance  (m)
                              3-60

-------
                                 Figure  3—41
      10
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                         Model: COM
                         Test: SUBDIVISION COMPARISON
                         Stability: D
                         Source Size: 150m X 150m, <75m X 75m)
                         Source Heiphfc Om
                         wind Speed: 2 m/s
                         Wind Direction: 360
                         Total Source Strength: 1 kg/s
                              4<75m X 75m) (o)
                              150m X 150m (»)
           0
500
1000
1500
2000
                   Downwind  Distance  (m)
                                  3-61

-------
      10 6-i
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-------
      10'-q
                             Figure  3—43
                Model: COM
                Test: FAR-FIELD CONVERGENCE
                Stability: D
                Source Size: 75m X 75m, 150m X 150m, 450m X 450m
                Source Height Om
                Wind Speed: 2 m/s
                Wind Direction: 360
                Total Source Strength: 1 kg/3
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                 1     234567
                  Downwind  Distance  (km)
8
                               3-63

-------
10*i
                               Rgure 3—44
10'
                         Modek VALLEY
                         Test- STABILITY COMPARISON
                         Stability: D. B. F
                         Source Size: 150m X 150m
                         Source Height: Orn
                         Wind Speed: 2 m/s
                         Wind Direction: 360
                         Totd Source Strength: 1 kg/s
                                           STABIUTY F
                                           STABILnY D
                                           STABILITY B
               500        1000      1500       2000
             Downwind   Distance  (m)
                          3-64

-------
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                               Figure  3—45
                        Model: VALLEY
                        fob SUBDIVISION COMPARISON
                        Stability: 0
                        Sourt* Size: 150m X 150m. 4<75m X 75m)
                        Source Heiaht Om
                        Wind Speed: 2 m/s
                        Wind Direction: 360
                        Total Source Strength: 1 kg/e
4<75m X 75m) (o)

150m X 150m (»)
                     500       1000      1500       2000
                  Downwind  Distance   (m)
                              3-65

-------
     105n
                                    Figure 3—46
                              Model: VALLEY
                              Teat SOURCE HEIGHT COMPARISON
                              Stability: 0
                              Source Size:  150m X 150m
                              Source Height: Om, 10m
                              Wind Speed: 2 m/s
                              Wind Direction: 360
                              Total Source Strength: 1 kg/*
CD
                                                 Om (*)

                                                10m (o)
      10 4-
          0
   500       1000       1500      2000
Downwind  Distance  (m)
                                3-66

-------
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                   Mod«l: VALLEY
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                   Source Heiahb  Om
                   Wind Speed: 2  m/3
                   Wind Direction:  360
                   Total Source Strength: 1 kg/e
                                            150m. 450m X 4SOm
                                                   75m  X 75m  (»)

                                                   150m X 150m (o)
                                                   450m X 450m(x)
                 If      •      I      I      I   •   I
           01      234567

                 Downwind  Distance   (km)
                                                          8
                              3-67

-------
4.0 COMPARISON OF MODEL PREDICTIONS WITH EXPERIMENTAL DATA



    Predictions for  the short-term area  source models  have been  compared to



measured concentrations for an  experimental  data set involving  releases of an



inert gaseous  tracer within an isolated stand  of trees/  surrounded by open



grassland.  These experiments produced a distributed emission source which can



be  simulated  as  an  area  source.   Comparisons  of  observed  and  predicted



concentrations for  this small  set of experiments provide  an informative test



of model performance.








4.1 Database Description



    A  review  of   available   information  from  field   measurement  programs



characterizing observed concentrations  in the vicinity of area source releases



was recently carried  out  as part of  a larger effort  to identify  and acquire



databases for  the evaluation  of air toxics models (Zapert, et al., 1989).  The



criteria which were used to select databases for model  evaluation included the



following:






    •  Reliable emissions data



    •  Site-specific meteorological measurements



    •  Adequate spatial resolution of ambient concentrations





    No  suitable  databases  representing  actual  sources  of toxic  pollutant



emissions  were  identified.   In  general,  measurement  programs  for  actual



sources  are characterized  by  uncertain  emissions  estimates and  poor spatial



resolution (concentration measurements at only two or three locations).



    The  five  databases chosen  for air toxics  model evaluation  all represent



measurement programs with controlled, artificial releases designed to simulate



actual  pollutant   releases.    Four  of  these  databases  involve  dense  gas



(heavier-than-air) releases and are not suitable for the present study.






                                      4-1

-------
    The database selected for  testing  area source  models  represents a  series



of  tracer  experiments  conducted   in  south-central  Washington  in  1982-83



(Allwine, et al.,  1985).  The  inert gaseous  tracer sulfur hexafluoride  (SFg)



was released from  an  array  of  points  within  an isolated  stand of  oak  trees.



The stand covered  an  area of 1.5 hectares  (15,000  m2)  to an average height of



8 m.   The experiments were  designed primarily to  estimate  isoprene emissions



from  the  forest  canopy.    By  measuring  SFg  and  isoprene  concentrations



simultaneously, the  isoprene emission  rate could  be  inferred, assuming  that



SFg  and isoprene  were both emitted  uniformly from  the  canopy.   Integrated



one-hour concentrations were measured  at sampling  points  approximately  100 m



downwind of  the release area.   Samplers were  deployed  at different locations



depending  upon  the   wind  direction.   The  tracer  release  was  initiated  10



minutes  before  ambient  sampling  began,  and continued  until  sampling  was



completed.  Wind speed, wind direction and temperature  measurements were also



collected  during  the  experiments.   A schematic  diagram  of  the  experimental



configuration is provided in Figure 4-1.



    Thirteen  tracer  experiments  from  this  program  were  chosen  for  model



evaluation.   Three tests  were  excluded because  the wind  direction  shifted



during  these  experiments and the peak measured concentrations occurred at  the



end  of  a  line of   samplers.   Emission  characteristics  and  meteorological



conditions  for the thirteen selected tests  are summarized  in Table 4-1.   All



of  the  experiments were conducted during the daytime,  when isoprene emissions



were  expected to be highest.  Consequently,  the tests  only represent unstable



and   neutral  conditions.    This  is   a  serious  limitation   for   the  present



application,  since  worst-case  short-term impacts from  near-ground emission



sources are expected  during  stable conditions.
                                      4-2

-------
               Sampling points
                                              N
               Sampling points

              Met.
              station
                    Release*
                points
                                                Met.
                                                station
                                    lOOm
                        FIGURE 4-1
ISOPRENE FLUX EXPERIMENT SAMPLING GRID, RELEASE POINTS AND WOODLOT
                            4-3

-------
                              TABLE 4-1

           METEOROLOGICAL AND EMISSION CHARACTERISTICS FOR
            TRACER RELEASE EXPERIMENTS IN A FOREST CANOPY
Test Number
   Tracer
Emission Rate
    (g/s)
Wind Speed
  (m/s)
Atmospheric
 Stability
   Class
     1
     2
     3
     4
     5
     6
     7
     8
     9
    10
    11
    12
    13
    .102
    .074
    .104
    .084
    .081
    .089
    .094
    .097
    .095
    .093
    .092
    .113
    .112
   1.0
   3.6
   5.8
   6.2
   2.2
   8.5
   1.5
   1.1
   1.3
   1.6
   1.8
   2.2
   3.2
     D
     D
     D
     D
     B
     D
     D
     C
     C
     C
     C
     D
     D
                                 4-4

-------
    While  these  forest-canopy  experiments  represent  a  useful  database  for

testing area source  models,  the  experimental configuration  introduces  a number

of  complicating  factors.    The  simple  modeling scenario  assumes that tracer

emissions will become  thoroughly mixed within the canopy and evolve out of the

top of the  source region.   The  following items contribute  to the uncertainties

involved in modeling these experiments:


    •  Dispersion within the canopy  is probably incomplete, and emissions
       will not occur  uniformly  across the source region.

    •  Thermal stratification may "trap"  some of  the  tracer within  the
       canopy.  Emissions  to ambient  air  may persist  for several  hours
       after the tracer source is shut off.

    •  In the trunk space  below the  canopy, winds  may  transport some of
       the tracer material out of the source region.

    •  Enhanced turbulence  is  likely  in the  lee  of the   source  region.
       The  forest grove may produce  the equivalent of  a  "building  wake"
       as ambient air  moves over and  around this obstacle.


The  effects of  these complicating  factors are  difficult to quantify.   Some

will  tend  to  increase  observed  tracer  concentrations,  others   to  lower

concentrations.   The   modeling  approach  does  not account for  any  of  these

factors.  The  source  is modeled  as  four  adjacent area source  squares,  each

61 x 61 m,  comprising  a  total  area  of  1.5  hectares.   The  source  emission

height was chosen as 8 m,  the average canopy height.



4.2 Results

    Predicted and observed tracer concentrations were compared for each tracer

experiment.   Statistics  were  computed both  for  event-by-event  comparisons

(paired in time) and for measures based on unpaired comparisons.  Observed and

predicted  concentrations  were  divided  by  the  tracer emission  rate before

computing statistics.
                                      4-5

-------
    4.2.1 Unpaired Comparisons



    The  maximum observed  and predicted  normalized  concentration values  are



illustrated in Figure 4-2.  Each vertical  bar spans the range of  the thirteen



maximum  values.   All  five of  the short-term  models overpredict  the observed



range of maximum values.   The  highest observed maximum value  (over all tests)



is 1.5 x 10™* s/m^,  while maximum predicted  values range  from 2.4 x 10~4 s/m3



by FDM up to 7.3 x 10~* s/m3 by RAM.



    The  range  of  maximum   values   predicted  by  FDM  and  SHORTZ  overlaps



considerably  with  the range of  observed values.   The lowest 8  or  9  maximum



values predicted by these  models fall in the same range as the top 10 observed



maxima.  By contrast,  most of the maximum values predicted by  ISC,  PAL and RAM



exceed the  highest observed value.   Overall, the  maximum  values  predicted by



FDM are  higher  than  the maximum observed  values by  roughly  a factor  of 1.6,



SHORTZ over-predicts by  a factor of 2, and  the remaining models by a factor of



4 to 5.








    4.2.2 Paired Comparisons



    The  maximum observed  and predicted  normalized  concentration values  for



each  experiment are  summarized  in Table 4-2.   The bias toward over-prediction



illustrated  in  Figure 4-2  is again evident in Table 4-2.   Lack of correlation



is  also  apparent, as events with peak  predicted  values do  not coincide with



high observed values.  For example, Test 1 has the  highest  predicted value for



all   five   models,   but  has   the   second-lowest   observed   maximum   value.



Correlation   coefficients  for  observed  and predicted  maximum  values  are



negative for all  five  models.  (By  contrast, the  correlation between maximum



values  predicted  by  any  two models  exceeds 0.95.   Inter-model correlation



results   from  the  use  of  the  same meteorological  inputs  for  all of  the



models.)  The  relative differences  between  maximum  observed and predicted






                                      4-6

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-------
                                  TABLE 4-2

                  MAXIMUM OBSERVED AND PREDICTED NORMALIZED
                     TRACER CONCENTRATION FOR EXPERIMENT

Test
Number
Observed
Concentration
(l
-------
values for each event are summarized in Table 4-3.  For FCM,  predicted maximum



values agreed  within a factor of 2  with maximum observed values  for  eight of



the 13 experiments.   All  five models  overpredicted  by more  than a factor of



1.25 for the majority of events.



    While  maximum  predicted values   are  closely  tied  to   meteorological



conditions,  observed  maximum values  do  not  show  a  similar  pattern.   The



highest observed values occurred  for tests  10  and 11  (C stability,  low wind



speed), but  tests  8  and  9  with  similar meteorology have  low observed maximum



values.  Three  experiments  with D stability  and low wind  speeds (tests  1,  7



and  12)  have  high  predicted  concentrations  but  relatively  low  observed



values.  The  Turner  stability method  (based on wind  speed  and  cloud  cover)



does not  provide an  effective method of  classifying  the observed  dispersion



behavior for these experiments.



    Summary - Comparisons of  observed and predicted maximum concentrations for



the 13 one-hour experiments demonstrate systematic overprediction by  all five



short-term  models.    FDM  showed  the  least  bias,  over-predicting by roughly a



factor of  1.6.   Maximum values  predicted by  FDM were  within  a factor of 2 of



the observed  maximum  for 8 of 13  events.  The range of  maximum predicted by



SHORTZ was  about a  factor of 2  higher  than observed;  predicted  and  observed



maxima agreed within  a factor of 2 for  6  events for SHORTZ.   ISC, PAL and RAM



all overpredicted the range of maximum values by more than a  factor  of 4.  For



these three models,  predicted and observed values agreed  within a factor of 2



for 4 or 5 events.   RAM overpredicted by more than a factor of 1.25  for  all 13



events.  Paired comparisons  showed  negative correlation between  observed and



predicted maximum values for all  models.
                                      4-9

-------
                              TABLE 4-3

               SUMMARY OF RELATIVE DIFFERENCES BETWEEN
                OBSERVED AND PREDICTED MAXIMUM VALUES
Ratio (R) of Predicted
 to Observed Maximum
    Concentration
ISC
FDM
PAL
RAM
SHORTZ
R > 2
2 > R > 1.25
1.25 > R > 0.8
0.8 > R > 0.5
0.5 > R
9
2
2
-
*»
5
2
2
4
_
8 9
2 4
3
- -
_ M
6
2
3
1
1
                                 4-10

-------
5.0 CONCLUSIONS AND RECOMMENDATIONS



5.1 Conclusions



    A  number  of  existing  dispersion  models  are  available  for  estimating



ambient  concentrations due  to  area source  emissions  from landfills,  waste



disposal areas  and contaminated  sites.  Three  basic  methods  of  calculating



concentrations due  to area sources  are employed by  existing models:   virtual



point  source,  line source, and  upwind  (numerical)  integration.   A group  of



five  short-term models  (FDM,  ISCST,  PAL,  RAM,  SHORTZ)  and three  long-term



(sector average) models  (ISCLT,  CDM, VALLEY)  incorporating these methods  were



selected for evaluation in the present study.



    None of these dispersion models incorporates a source  algorithm capable  of



estimating evaporative emission  rates  from  a landfill or  waste  disposal area.



For  many  applications,  the  variation  of  emissions   as  a  function  of



environmental  factors  (ambient  or  ground  surface  temperature,   wind  speed,



moisture)  is   a  major  source   of   uncertainty   for   determining   ambient



concentrations.



    Example  Applications.   Each  of  the eight models  was  applied  to  predict



ambient  concentrations  for  a  base  case hypothetical scenario and  for several



variations designed to  examine whether model  predictions  are consistent  with



mathematical and physical principles.



    The base case  represented  a  single 150 x  150  m ground level area  source,



with  receptors located downwind  at  distances ranging  from  100 m  to 1,575  m



from the source center.   Among the short-term models, FDM generally predicted



the lowest concentrations.  The  other four models'  predictions  varied in rank,



depending  upon  distance  and  stability  class.   At   large distances,   RAM



consistently predicted the highest concentrations.



    All of  the models  except  RAM showed reasonable  far-field behavior.   PAL



predictions  were  physically   reasonable  and  consistent  with  source-receptor






                                      5-1

-------
geometry  for  ail   of  the  configurations  analyzed.   FDM   produced  small



inconsistencies  (generally  10  percent or  less) for  two  tests  sensitive  to



source geometry.   Predictions by SHORTZ  and ISCST  in the near-field did not



accurately reflect source-receptor  geometry for one or more comparisons.   RAM



did not give physically reasonable  results for  either near-field  or far-field



predictions.



    None  of the  three long-term average  models consistently  gave  reasonable



results.   Comparisons  of  predictions   for  different  scenarios   indicated



inaccuracies of  10  percent  or more  in  calculated concentrations.   Serious



deficiencies in VALLEY were indicated by several tests.   ISCLT  and  COM gave



reasonable  results  for most  comparisons.   ISCLT,  however,   predicted  large



differences for the subdivision comparison, while CDM gave  erratic  results for



the far-field convergence test.



    Comparison   with   Observed  Concentrations.    Observed   and   predicted



concentrations were  compared  for a data base of thirteen one-hour experiments



involving tracer  releases within an isolated grove of trees.   The  release was



modeled as  a 1.5 ha area source.   All five of the short-term models predicted



higher maximum tracer concentrations than were measured roughly 100  m downwind



of  the  source  region.   FDM and  SHORTZ overpredicted  by  a factor of 2 or less,



while ISCST, PAL and RAM overpredicted by  roughly  a factor of 4.   All  of the



models  showed  poor correlation between observed and  predicted maximum values,



paired by event.



    Recommendations.   Overall,  the  short-term  models which  employ algorithms



based  on  the  line-source  method  (FDM,  PAL) appear  to provide  an  adequate



treatment  of  near-source geometry  and reasonable far-field behavior.  Subject



to  further performance  testing,  either FDM or PAL  is  recommended  for use with



near-ground area  sources.   (The area source algorithm in FDM is not a separate



modular component of the  model.   This algorithm  is coupled to line source and






                                      5-2

-------
dispersion algorithms  taken from CALINE.  which uses  a modified  form of  the



Pasquill-Gifford rural  dispersion coefficients.)   It  may also  prove feasible



to  modify either  SHORTZ  or  ISCSI  to  provide  more  reasonable  near-field



predictions.   The RAM  model  is not recommended for application  to an isolated



area source.



    In light  of the widespread use of ISCST for  regulatory applications,  the



feasibility of modifying or  replacing the ISC  area source  algorithm deserves



serious consideration.   The inaccuracy of  near-field  area  source predictions



by ISCST is sufficiently large that the users guide recommends subdividing  the



source  if receptors   are  placed  near  that source.   In  the  present  study,



results have  clearly demonstrated  that  ISCST  does not  account  properly  for



source-receptor geometry at near-field receptors.



    The  long-term  average  results  indicated  that ISCLT   and  CDM  generally



provide  adequate treatment  of area  source dispersion,  but  each  model  has



specific  shortcomings.   Subject to  further  testing,   either  ISCLT  or CDM  is



recommended.   The FDM, LONGZ  and AQDM models  contain area  source  algorithms



similar to ISCLT and are expected  to  produce similar  predictions.   Potential



modifications to  correct specific  model  shortcomings  should be investigated.



The VALLEY model is  not recommended for application to area sources.
                                      5-3

-------
                                  REFERENCES

Allwine,  G.,  Lamb,  B.  and  Westberg, H.,  Application  of Atmospheric  Tracer
    Techniques for Determining Biogenic  Hydrocarbon Fluxes from an Oak Forest,
    The Forest - Atmosphere  Interaction, Ed.  Hutchison, B.A.  and Hicks, B.B.,
    D. Reidel Publishing Company, Boston, 1985.

Benson,  P.E., CALINE3  -  A  Versatile  Dispersion  Model  for Predicting  Air
    Pollutant Levels Near Highways  and Arterial Streets, PB80-220841, November
    1979.

Bjorklund, J.R. and  Bowers,  J.F.,  User's Instructions for the SHORTZ and LONGZ
    Computer  Programs,  Volumes   I  and II,  EPA-903/9-82-004a  and 004b,  March
    1982.

Hurt, E.W., Valley Model User's  Guide, EPA-450/2-77-018, September 1977.

Gschwandtner,  G.,  Eldridge,  R.  and  Zerbonia,  R.,  "Sensitivity Analysis  of
    Dispersion  Models   for  Point  and  Area  Sources,"  JAPCA, Vol.  32,  #10,
    October 1982.

Guideline on Air Quality Models   (Revised), EPA-450/2-78-027R, July 1986.

Hodanbosi,  R.F.  and Peters,  L.K.,  "Evaluation of RAM  Model for  Cleveland,
    Ohio," JAPCA,  Vol.  31, #3, March 1981.

Hwang,  S.T.,  "Methods  for  Estimating  On-Site  Ambient Air  Concentrations  at
    Disposal Sites," Nuclear and Chemical Waste Management, Vol. 7, 1987.

Irwin, J.S. and Brown,  T.M.,  "A Sensitivity Analysis  of the Treatment of Area
    Sources by the Climatological Dispersion Model," JAPCA, Vol.  35,  #4, April
    1985.

Irwin, J.S.,  Chico,  T.  and  Catalano, J., CDM  2.0  — Climatological  Dispersion
    Model, EPA/600/3-85/029, PB86-136546, November 1985.

Rao,  K.S., User's  Guide for PEM-2:  Pollution Episodic  Model   (Version  2),
    EPA/600/8-86/040, PB 87-132   098, December 1986.

Schere, K.L. and Demerjian,  K.L., User's Guide for  the  Photochemical  Box Model
    (PBM), PB85-137164, November 1984.

Schulze,  R.,   Practical Guide   to  Atmospheric  Dispersion  Modeling,  Trinity
    Consultants, Inc.,  Dallas, 1989.

Schulze,   R.H.,  Ed.,   "All   Area  Source  Models   Are Not   Created  Equal,"
    Atmospheric  Diffusion Notes,  Trinity  Consultants, Inc.,  Issue  #7,  July
    1982.

Scire,  J.S.,  Lunnann,  F.W.,  Bass,  A.  and Hanna,  S.R.,  User's  Guide  to  the
    Mesopuff  II Model  and Related Processor Programs,  EPA-600/8-84-013, April
    1984.
                                      R-l

-------
Simnon, P.B., Patterson,  R.M.,  Ludwig,  F.L.  and Jones, L.B., User's  Manual for
    the   APRAC-3/Mobilel   Emissions    and   Diffusion    Modeling    Package,
    EPA/909-9-81-002, July 1981.

Turner, D.B., Workbook of  Atmospheric  Dispersion Estimates, EPA Office  of Air
    Programs, Pub.  # AP-26, 1970.

Turner,  D.B.  and  Novak,   J.H.,   User's  Guide  for  RAM,  Vol.  1,   Algorithm
    Description and Use,  EPA-600/8-78-016A,  November 1978.

Wackter,  D.J.  and  Foster, J.A.,  Industrial  Source  Complex  (ISC)  Dispersion
    Model User's Guide - Second  Edition (Revised), Vol. 1,  EPA-450/4-88-002a,
    December 1987.
                                      R-2

-------
                                   TECHNICAL REPORT DATA
                            (Please read fnstmciians on tHe reverse before completing)
  REPORT NO.
   EPA-450/4-89-020
                              2.
                                                            3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
  Review and  Evaluation of Area  Source Dispersion
  Algorithms  for Emission Sources  at Superfund Sites
                                   5. REPORT DATE

                                       November 1989
                                   6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
                                                            I. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
  TRC Environmental  Consultants,  Inc.
  800 Connecticut Boulevard
  East Hartford,  CT 06108
                                                            10. PROGRAM ELEMENT NO.
                                   11. CONTRACT/GRANT NO.
                                                               68-02-4399
12. SPONSORING AGENCY NAME AND ADDRESS
                                                            13. T
  U.S.  Environmental Protection Agency
  Office  of Air Quality Planning  and Standards
  Research  Triangle Park, N. C. 27711
                                                               •*ee of REPORT A
                                                               Final Report
                                                                            NO PERIOD COVERED
                                   14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
  EPA  Project Officer:
Jawad S. Touma
16. ABSTRACT
     This  report examines  air  quality dispersion modeling  algorithms  and  related
     technical  issues associated with  estimating ambient concentrations from  area
     sources  at  Superfund  sites.   The report  describes  the area  source emission
     characteristics  associated  with  Superfund  sites  and  provides  a  review  of
     existing,  available techniques for modeling area sources.  It also describes the
     results  of applying five  short-term and  three  long-term  area  source models to
     a number of  example applications and one field data base  in order to  compare the
     magnitude  of concentration predictions  and  test whether concentration estimates
     are consistent with mathematical  and physical principles.  The report provides
     conclusions  and recommendations  from this study.
17.
                                KEY WORDS AND DOCUMENT ANALYSIS
                  DESCRIPTORS
                     b.lOENTIFIERS/OPEN ENDED TERMS  C.  COSATI Field/Group
  Air  Pollution
  Hazardous  Waste Assessment
  Toxic Air  Pollutants
  Air  Quality Dispersion Models
                       Dispersion  Modeling
                       Meteorology
                       Air Pollution  Control
   13 B
18. DISTRIBUTION STATEMENT

  Release  Unlimited
                     19. SECURITY CLASS (This Report!
                       Unclassified
21 NO OP PAGES
   120
                                              20 SECURITY CLASS (Tins page}
                                                Unclassified
                                                                          22. PRICE
EPA Form 2220-1 (Rev. 4-77)
                      PREVIOUS EDITION IS OBSOLETE

-------
                                             July 1991
                         ERRATA FOR

REVIEW AND EVALUATION OF AREA SOURCE DISPERSION ALGORITHMS

           FOR EMISSION  SOURCES AT  SUPERFUND  SITES
        Office of Air Quality Planning and Standards
                 Technical Support Division
              Research Triangle  Park,  NC   27711

-------
                         LIST OF ERRATA
 REVIEW AND EVALUATION OF AREA  SOURCE  DISPERSION ALGORITHMS  FOR
               EMISSION SOURCES AT SUPERFUND SITES
                        EPA-450/4-89-020
                         NOVEMBER,  1989

                 NTIS ACCESSION NO. PB90-142753
The following pages are replaced:

1.   Page ix

2.   Page 2-22

3.   Page 4-1

4.   Page 4-4

5.   Pages 4-6 through 4-10

6.   Page 5-2

7.   Page R-l

-------
                                LIST OF TABLES

TABLE                                                                      PAGE

 1-1          AREA SOURCE CHARACTERISTICS 	       1-4

 2-1          CHARACTERIZATION OF AREA SOURCE ALGORITHMS IN EXISTING
                MODELS	       2-8

 3-1          SUMMARY OF SENSITIVITY TEST RESULTS FOR SHORT-TERM
                MODELS	      3-21

 4-1          METEOROLOGICAL AND EMISSION CHARACTERISTICS FOR TRACER
                RELEASE EXPERIMENTS IN A FOREST CANOPY  	       4-4

 4-2          MAXIMUM OBSERVED AND PREDICTED NORMALIZED TRACER
                CONCENTRATION FOR EXPERIMENT  	       4-8

 4-3          SUMMARY OF RELATIVE DIFFERENCES BETWEEN OBSERVED AND
                PREDICTED MAXIMUM VALUES  	       4-9
                                     —ix-

-------
sources.   Two published  articles were  identified  which discussed  alternative




approaches to modeling area  sources.   Neither approach  represents  a  computer




algorithm suitable for evaluation in the present  study.




    The models suggested  by Hwang (1987) and Chitgopekar et al.  (1990)  attempt




to  resolve   some  of  the known  shortcomings of  the currently  utilized  area




source dispersion  algorithms.   The  discussion  by Hwang  is  theoretical  but




examines both a Gaussian approach  and one based on  transport  equations in the




atmospheric  boundary  layer.   Chitgopekar  et al.  (1990) attempts  to  resolve




problems of  near-field prediction through the use of  a "top-hat"  formulation.




    Chitgopekar et  al.  (1990)  presents  an  area  source  model  developed  in




response  to  problems  with virtual point models  in the  near-field for  area




sources.   These authors  state that  "the most rigorous"  treatment  of  Gaussian




dispersion from  area  sources would  be to  model  them  as a  dense matrix  of




multiple point sources.   This idea  can be conceptualized as increasing matrix




density until, in the  limit,  inter-point spacing goes to zero and  every point




in the area  is  emitting.  This  approach is  computationally  intensive  and  is




rarely used  in the  standard  models.   (The finite  line segment approach in FDM




or  PAL  is  mathematically  equivalent  but  far  more  efficient,  if  Gaussian




dispersion is assumed.)   Virtual point  source methods do not  require extensive




computations and can be  simplified to allow manual  calculation.  However, the




virtual point source method  should not be used  if the  source  width is greater




than  40% of  the  distance  between the  source   centerpoint  and  the  receptor




(Hwang,  1986 as  cited  in  Chitgopekar  et   al.,  1990).   This  is a  serious




limitation due to  the fact  that the  region of  interest in  many  area source




pollutant dispersion and  exposure situations is  in  the near-field.




    The  treatment by Chitgopekar et  al.  is  to  divide  the  dispersion  of  the




plume into high frequency and low frequency components.  A low  pass filter is




used  so  plume meander is  included but  small turbulent scales  are  not.  The






                                     2-22

-------
4.0 COMPARISON OF MODEL PREDICTIONS WITH EXPERIMENTAL DATA




    Predictions for  the short-term  area  source models  have  been compared  to




measured concentrations for  an  experimental  data set involving  releases  of  an




inert gaseous  tracer within an isolated stand  of  trees,  surrounded by  open




grassland.  These experiments produced  a  distributed emission source which can




be  simulated  as  an  area  source.   Comparisons  of  observed  and  predicted




concentrations for  this small  set of experiments provide  an  informative test




of model performance.








4.1 Database Description




    A  review  of   available  information  from  field   measurement   programs




characterizing observed concentrations  in the vicinity of area source releases




was recently carried  out  as  part of  a  larger effort  to identify and  acquire




databases  for  the evaluation of air toxics  models.   The  criteria  which were




used to select databases for model evaluation included the following:






    •  Reliable emissions data




    •  Site-specific meteorological measurements




    •  Adequate spatial resolution of ambient concentrations






    No  suitable  databases   representing  actual  sources  of  toxic  pollutant




emissions  were  identified.    In  general,  measurement  programs  for  actual




sources are  characterized by uncertain emissions estimates  and poor  spatial




resolution (concentration measurements at only two or three locations).




    The five  databases chosen  for air toxics  model evaluation  all  represent




measurement programs  with controlled,  artificial releases designed to simulate




actual  pollutant  releases.    Four  of  these   databases   involve   dense  gas




(heavier-than-air) releases and are not suitable for the present study.
                                      4-1

-------
                   TABLE 4-1

METEOROLOGICAL AND EMISSION CHARACTERISTICS FOR
 TRACER RELEASE EXPERIMENTS IN A FOREST CANOPY
Test Number
1
2
3
4
5
6
7
8
9
10
11
12
13
Tracer
Emission Rate

-------
    4.2.1 Unpaired Comparisons




    The  maximum  observed  and predicted  normalized concentration  values  are




illustrated in Figure 4-2.  Each  vertical bar spans the range  of the thirteen




maximum  values.   Four  of the  five models  overpredict the maximum  observed




value and all five  models overpredict the minimum observed value.  The highest




observed maximum  value  (over all  tests)  is  1.5  x  10~^  s/rn-^,  while  maximum




predicted values range  from  1.2  x 10~4 s/m^ by  FDM  up to 3.2  x 10~~4  s/m^  by




RAM.




    The  range  of  maximum  values  predicted by  FDM  is  within  the range  of




observed values.   The lowest  12  maximum values predicted by SHORTZ  fall in the




same  range  as  the  top  10 observed  maxima.   By contrast,  most  of  the  maximum




values predicted by  ISC,  PAL  and RAM exceed the  highest  observed  value.   The




median value predicted  by FDM  is higher  than the maximum  observed median by




roughly  a  factor  of 1.3,  SHORTZ  overpredicts by  a  factor  of  1.7,   and  the




remaining models  by factors of between 2.7 and 3.2.









    4.2.2 Paired  Comparisons




    The  maximum  observed  and predicted  normalized concentration   values  for




each  experiment are  summarized  in Table 4-2.  The bias toward overprediction




illustrated in Figure 4-2  is  again evident in Table 4-2.  Lack of  correlation




is also  apparent, as events  with  peak  predicted values do  not  coincide  with




high  observed values.   For example,  Test  1 has the highest predicted value for




all   five   models,   but  has   the  second-lowest   observed  maximum   value.




Correlation  coefficients  for  observed   and   predicted  maximum  values  are




negative  for  all  five  models.    The  relative   differences  between  maximum




observed and predicted  values for  each event are summarized in Table 4-3.   For




FDM,  predicted  maximum values  agreed within  a factor  of  2  with  maximum
                                      4-6

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-------
                                TABLE  4-2

                 MAXIMUM OBSERVED AND PREDICTED NORMALIZED
                   TRACER CONCENTRATION FOR  EXPERIMENT
Test
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
# Closest
# Closest
Observed
Concentration
(10~6 s/m3)
24.5
87.8
33.7
74.6
88.5
58.5
67.1
21.7
37.0
99.8
151.9
45.9
42.8
to Observed
to Observed
ISC
292.
174.
101.
101.
109.
71.
172.
227.
190.
162.
143.
180.
181.
0
1
FDM
123.
88.
55.
53.
50.
36.
76.
94.
82.
70.
63.
81.
81.
8
8
PAL
295.
149.
89.
88.
102.
57.
169.
222.
186.
159.
140.
191.
157.
3
3
RAM
322.
232.
139.
135.
120.
91.
206.
304.
253.
182.
160.
187.
209.
1
1
SHORTZ
163.
101.
64.
60.
38.
42.
98.
125.
103.
97.
86.
104.
102.
1
0
and Greater
                                   4-8

-------
                              TABLE 4-3

               SUMMARY OF RELATIVE DIFFERENCES BETWEEN
                OBSERVED AND PREDICTED MAXIMUM VALUES
Ratio (R) of Predicted
 to Observed Maximum
    Concentration
ISC
FDM
PAL
RAM
SHORTZ
         R >  2

     2 > R > 1.25

    1.25 > R > 0.8

     0.8  > R  >  0.5

       0.5 > R
 7       3

 3       4

 3       1

         4

         1
         7

         2

         4
         8

         4

         1
          5

          2

          2

          3

          1
                                 4-9

-------
observed values  for  9 of  the 13  experiments.   Three models  overpredicted  by




more than a factor of 2.0 for the majority of events.




    While  maximum   predicted  values  are  closely   tied   to  meteorological




conditions,  observed maximum values  do  not  show a similar  pattern.  The




highest observed  values  occurred  for  tests 10  and 11 (B stability,  low wind




speed), but  tests 8  and  9 with  similar meteorology have  low  observed  maximum




values.  The Turner  stability method  (based  on wind speed and cloud cover)




does not  provide an  effective  method of  classifying  the observed  dispersion




behavior for these experiments.




    Summary - Comparisons of observed and predicted maximum  concentrations for




the  13  one-hour  experiments demonstrate a general  tendency  towards  systematic




overprediction by all five  short-term  models.  FDM  showed  the least  bias,




overpredicting the median maximum  value  by roughly a factor  of  1.3.   Maximum




values predicted by FDM were within a  factor of 2 of  the observed maximum for




9  of 13 events.  The median maximum  predicted by  SHORTZ was a  factor of 1.7




higher than observed; predicted  and observed maxima agreed within a  factor  of




2  for  7  events   for  SHORTZ.  ISC,  PAL  and RAM  all  overpredicted the median




maximum  value by approximately a  factor  of  3.   For  these  three  models,




predicted  and observed values agreed within a  factor  of  2  for 5 or  6  events.




RAM overpredicted for all 13 events.
                                     4-10

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geometry  for  all   of   the  configurations  analyzed.   FDM   produced   small




inconsistencies  (generally  10  percent  or  less)  for  two  tests  sensitive  to




source geometry.   Predictions by SHORTZ  and ISCST  in the near-field did not




accurately reflect source-receptor geometry  for one or more comparisons.   RAM




did not give physically  reasonable  results for either near-field or  far-field




predictions.




    None  of  the  three  long-term average  models  consistently  gave  reasonable




results.    Comparisons  of  predictions   for  different  scenarios   indicated




inaccuracies of   10  percent  or more  in  calculated concentrations.  Serious




deficiencies in VALLEY were indicated by several tests.   ISCLT  and CDM  gave




reasonable  results  for  most  comparisons.   ISCLT, however,  predicted  large




differences for the subdivision comparison, while  CDM gave  erratic  results for




the far-field convergence test.




    Comparison   with  Observed   Concentrations.     Observed   and    predicted




concentrations were  compared  for a  data  base of  thirteen one-hour  experiments




involving tracer releases within an  isolated grove  of trees.   The  release was




modeled as  a 1.5  ha area  source.  All  five of  the short-term models tend to




predict higher maximum tracer concentrations than were measured roughly  100 m




downwind of the  source region (Table 4-2).  FDM  and SHORTZ overpredicted by a




factor of 2  less than  half  of the test cases,  while ISCST,  PAL  and  RAM




overpredicted more  than  half of the cases by a  factor  of  2 or more.  All of




the  models  showed  poor  correlation  between observed  and  predicted maximum




values, paired by event.




    Recommendations.   Overall,  the  short-term models  which employ  algorithms




based  on the  line-source  method  (FDM,   PAL)  appear  to provide  an  adequate




treatment of near-source geometry and  reasonable  far-field behavior.  Subject




to further performance testing,  either FDM or PAL  is  recommended for use with




near-ground area sources.   (The area  source algorithm in FDM is not a  separate




modular component of the  model.   This  algorithm  is  coupled to  line source and




                                     j-2

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                                  REFERENCES

Allwine, G. ,  Lamb,  B.  and  Westberg,  H. ,  Application  of Atmospheric  Tracer
    Techniques for Determining  Biogenic Hydrocarbon Fluxes from an Oak Forest,
    The Forest - Atmosphere  Interaction, Ed.  Hutchison, B.A. and  Hicks,  B.B.,
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Benson,  P.E., CALINE3  -  A  Versatile  Dispersion  Model  for  Predicting  Air
    Pollutant Levels Near  Highways  and Arterial Streets, PB80-220841, November
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Bjorklund,  J.R. and  Bowers,  J.F., User's Instructions for the SHORTZ and LONGZ
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Burt, E.W., Valley Model User's Guide, EPA-450/2-77-018, September  1977.

Chitgopekar,  N.P.,  D.D. Reible  and L.J. Thibodeaux,''"Modeling  Short  Range Air
    Dispersion from  Area  Sources  of  Non-Buoyant  Toxics',1  AWMA,  August  1990,
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Gschwandtner,  G.,  Eldridge,  K.  and  Zerbonia,   R. ,  "Sensitivity  Analysis  of
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Guideline on Air Quality Models  (Revised), EPA-450/2-78-027R, July  1986.

Hodanbosi,   R.F.  and  Peters, L.K.,  "Evaluation of  RAM  Model  for  Cleveland,
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Hwang,  S.T.,  "Methods   for Estimating  On-Site  Ambient  Air Concentrations  at
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Irwin, J.S. and Brown,  T.M.,  "A Sensitivity Analysis of the  Treatment  of Area
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Irwin,  J.S.,  Chico,  T.  and Catalano, J., COM 2.0  — Climatological Dispersion
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Rao,  K.S.,  User's  Guide  for  PEM-2:   Pollution Episodic  Model   (Version 2),
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Schere, K.L.  and Demerjian,  K.L., User's Guide  for the Photochemical Box Model
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Schulze,  R.,  Practical  Guide  to  Atmospheric Dispersion  Modeling,  Trinity
    Consultants,  Inc.,  Dallas, 1989.

Schulze,  R.H.,  Ed.,   "All   Area  Source  Models   Are   Not Created  Equal,"
    Atmospheric Diffusion Notes,  Trinity  Consultants,  Inc.,  Issue  #7,  July
    1982.

Scire,  J.S.,  Lurmann,   F.W.,  Bass,  A.  and  Hanna,  S.R., User's Guide to the
    Mesopuff  II Model  and Related  Processor  Programs,  EPA-600/8-84-013,  April
    1984.

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