United States
             Environmental Protection
             Agency
            Office of Air Quality
            Planning and Standards
            Research Triangle Park NC 27711
EPA-450/4-86-002
February 1986
             Air
oEPA
Evaluation of
Mobile Source Air
Quality Simulation
Models

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                                   EPA-450/4-86-002
  Evaluation of Mobile  Source
Air Quality Simulation Models
                      By

             David Wackter and Paul Bodner
           TRC Environmental Consultants, Inc.
              800 Connecticut Boulevard
               East Hartford, CT06108
              Contract No. 68-02-3886
                                 U.S. Environmental Protection As—'
                                       r  T i "h-"-q~*"Y I . - iJ -- - '    ,, „
                                 ST i^o^ st««. r.o- iov-
                                 Cbiio, IL  60604
                   Prepared for
        U.S. ENVIRONMENTAL PROTECTION AGENCY
        Office of Air Quality Planning and Standards
          Monitoring and Data Analysis Division
           Research Triangle Park, NC 27711

                  February 1986

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This report has been reviewed by the Office Of Air Quality Planning And Standards, U.S. Environmental
Protection Agency, and 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-450/4-86-002

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                               TABLE OF CONTENTS
SECTION
  1.0
  2.0
      2.1
      2.2
      2.3
      2.4
      2.5
  3.0
      3.1

      3.2
      3.3
      3.4
      3.5
  4.0
      4.1
      4.2
      4.3
  5.0
      5.1
      5.2
      5.3

      5.4

      5.5
  6.0

  7.0
                                                          PAGE

 INTRODUCTION  	       1

 DESCRIPTION OF THE MOBILE MODELS  	       3
   CALINE3 	       3
   GMLINE  	       5
   HIWAY-2 	       5
   PAL	       6
   Modifications to the Mobile Models  	       6

 DESCRIPTION OF THE DATA SETS	      11
   California Department of Transportation -
     Santa Monica Freeway (1974)  	      12
   General Motors (1975) 	      15
   New York State Long Island Expressway - CO (1977) .      18
   New York State Long Island Expressway - SF6 (1976)      21
   SRI Highway 101 (1975)	      21

 STATISTICS APPROACH 	      27
   Pre-Analysis Data Handling Procedures 	      27
   Statistical Performance Measures  	      30
   Data Sets for Statistical Analysis	      34

 MODEL PERFORMANCE RESULTS 	      39
   Statistics for "N" Highest Values 	      39
   Statistics for Highest Concentration by Event ...      43
   Statistics for All Concentrations Paired in Time
     and Space	      53
   Statistics for Highest Concentration at Each
     Station	      57
   Examples of Statistics by Subsets 	      61

 SUMMARY AND CONCLUSIONS 	      69

 REFERENCES	      71
APPENDICES

   A

   B


   C

   D
STATISTICS FOR HIGHEST "N" VALUES

STATISTICS FOR VALUES PAIRED IN TIME, INCLUDING HIGH BY
EVENT AND PAIRED IN SPACE AND TIME

STATISTICS FOR HIGHEST CONCENTRATION BY STATION

STATISTICS FOR HIGHEST VALUES FOR VARIOUS PAIRINGS
                                      -111-

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                                LIST OF FIGURES

FIGURE                                                                     PAGE

  3-1     Monitoring Configuration for the California Department of
            Transportation CO Data Base (CAL74-CO)	     13

  3-2     Monitoring Configuration for the General Motors SFS Data
            Base (GM75-SF6)   	     16

  3-3     Monitoring Configuration for the New York CO Data Base
            (NYS77-CO)	     19

  3-4     Monitoring Configuration for the New York SF6 Data Base
            (NYS76-SF6)   	     22

  3-5     Monitoring Configuration for the SRI CO Data Base (SRI75-CO)
            CO Probes Shown in the Lower Diagram are at a Height of
               1 Meter	     23

  5-1     Difference of Averages of Highest "N" Observed and Predicted
            One Hour Concentrations	     42

  5-2     Average Difference of Highest Observed and Predicted One-Hour
            Concentrations (Event by Event) 	     48

  5-3     Average Difference of Highest Observed and Predicted One-Hour
            Concentrations (Paired in Time and Space) 	     56
                                      -iv-

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                                 LIST OF TABLES

TABLE                                                                      PAGE

  2-1     Comparison of Input Data Requirements for the Mobile Models .       4

  4-1     Statistical Estimators and Basis for Confidence Limits on
            Performance Measures  	      31

  4-2     Array of Performance Measures and Statistics Calculated for
            the "N" Highest (Unpaired) Mobile Data Sets (Where N is the
            Lesser of 25 or 25%)	      36

  4-3     Array of Performance Measures and Statistics Calculated for
            the Mobile Data Sets Paired in Time or Location	      38

  5-1     Comparison of "N" Highest Observed and Predicted One Hour
            Concentration Values (Unpaired in Time or Location) ....      40

  5-2     Comparison of "N" Highest Observed and Predicted Eight Hour
            Concentration Values (Unpaired in Time or Location) ....      44

  5-3     Comparison of Highest Observed and Predicted One Hour
            Concentration Values Event by Event (Paired in Time)  ...      46

  5-4     Comparison of Highest Observed and Predicted Eight Hour
            Concentration Values Event by Event (Paired in Time)  ...      51

  5-5     Comparison of All Observed and Predicted One Hour Concent-
            ration Values (Paired in Time and Space)	      54

  5-6     Comparison of Highest and Second Highest Observed and
            Predicted One Hour Concentration Values (Paired by Station)      58

  5-7     Comparison of Highest and Second Highest Observed and
            Predicted Eight Hour Concentration Values (Paired by Station)   60

  5-8     Comparison of Difference of Averages for "N" Highest Observed
            and Predicted SF6 Concentration Values (Unpaired in Time or
            Location) for Various Subsets, Data Base:  GM75, Averaging
            Time:  Half Hour	      62

  5-9     Comparison of Difference of Averages for "N" Highest Observed
            and Predicted CO Concentration Values (Unpaired in Time or
            Location) for Various Subsets, Data Base:  NYS77-CO,
            Averaging Time:  1 Hour	      64

  5-10    Comparison of Difference of Averages for Observed and
            Predicted SFS Concentration Values Paired in Time and Space
            for Various Subsets, Data Base:  GM75, Averaging Time:
            Half Hour	      65

  5-11    Comparison of Difference of Averages for Observed and
            Predicted CO Concentration Values Paired in Time and Space
            for Various Subsets, Data Base:  NYS77, Averaging Time:
            1 Hour	      67

                                       -v-

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1.0 INTRODUCTION

    EPA  is  involved  in  an  ongoing  study  to  systematically  evaluate  the

performance  of  air  quality  simulation  models  using  statistical  measures

recommended  by the American Meteorological  Society  (AMS).   Results  of  the

study,  in  conjunction  with  scientific  peer  review,  will  provide EPA  with

measures of  accuracy  and  the degree of  confidence that  can be  expected when

air quality models are applied  in regulatory  settings.  Mobile  source models

are one category of models to be  evaluated.

    In  this   study,  the  following  mobile  source  models  were  evaluated:
    •  CALINE3
    •  GMLINE
    •  HIWAY-2
    •  PAL
Section 2 of  this  report contains  a  brief description  of these  models  along

with  a  description of  the  procedures for implementing and testing  the models

and the unique input data requirements.

    The five data sets used for this evaluation are listed below:
       GM75 - General Motors Sulfate Dispersion Experiment (SF6)
       SRI75 - SRI, Highway 101 (CO)
       NYS76 - New York State, Long Island Expressway (SF6)
       NYS77 - New York State, Long Island Expressway (CO)
       CAL74  -  California  Department  of Transportation,  Santa  Monica
       Freeway (CO)
                                       -1-

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These data  sets  are described  in Section  3  along with  diagrams showing  the




locations of monitoring sites relative to the  roadways.




    The statistical approach is presented  in  Section 4.   Sets of observed  and




predicted  concentration values have  been  grouped in various  ways to  depict




performance for  the high end  of  the  frequency  distribution as  required  for




regulatory  applications,  as  well  as  performance for  the  full spectrum  of




observed and predicted values.




    In  Section  5 the  results  of  the  study are presented.   Summary tables of




the statistical  measures  recommended  by the AMS are  presented  for each model




and data base.   Examples  of the tables by subsets of meteorological conditions




and monitoring station (the  full set of which are shown in  the  appendices)  are




also presented.




    Four sets of appendices provide tables of statistical  results  for various




data  groupings.   Appendix  A contains tables  of High "N"  results.   Appendix B




contains the "All Paired" and "High by Event" analyses.   The  "High  by  Station"




tables  are shown  in  Appendix  C  and  single value comparisons  are  provided in




Appendix D.
                                       -2-

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2.0 DESCRIPTION OF THE MOBILE MODELS

    The four models  evaluated in the mobile  source category  include  CALINE3,

GMLINE, HIWAY-2, and  PAL.   As shown in Table  2-1,  each model requires similar

inputs to determine  concentrations.   These inputs include descriptions  of  the

highway site,  receptor locations  and  meteorology.   While  the inputs  to  each

model  are  similar,  different methods  are  used by  each  model  to  simulate

dispersion near a  roadway.   A brief summary of  each model  follows, along with

a description of necessary coding changes made by TRC.  The  reader  is  referred

to the individual user's guides for a detailed description of each model.



2.1 CALINE3

    The  CALINE3   model   (Benson,   1979)   was  developed  by   the   California

Department of  Transportation.   It  simulates dispersion of highway emissions by

dividing  individual  roadway  links  into  a  series  of  elements  from  which

incremental concentrations  are computed  using a finite  line  source equation.

The  incremental concentrations  are summed  to  obtain a  total  concentration

estimate at a particular receptor location.

    CALINE3  simulates the  region  directly  over  the  roadway  as   a  zone  of

uniform  emissions   and   turbulence  called  the  "mixing  zone."   This  zone

experiences  increased  dispersion   due  to  mechanical  turbulence  created  by

moving vehicles as well  as thermal turbulence created by hot vehicle exhaust.

CALINE3 adjusts the level of turbulence as  a  function of wind speed.   At  low

wind  speeds   residence  time  of  an  air  parcel  within  the   mixing  zone  is

increased,  resulting in turbulence  enhancement  through  the  use  of  a  larger

initial vertical sigma value.

    The CALINE3 model  includes  options  for  simulating  dispersion from  four

types  of  roadways:   at grade, elevated filled sections,  elevated bridges,  and

cut  or depressed  sections.   Multiple  lanes,  links  and  orientations  can be

simulated.
                                       -3-

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                                   TABLE  2-1

          COMPARISON OF INPUT DATA REQUIREMENTS FOR THE MOBILE MODELS*
Function/Parameter
   CALINE3
 GMLINE
 HIWAY-2
  PAL
SOURCE/SITE
  Number of Links, Lanes           X
  Link Coordinates                 X
  Link Height                      X
  Width of Highway or
    Mixing Zone                    X
  Median Width
  Traffic Volume                   X
  Emission Factor                  X
  Emission Rate
  Buoyancy Flux                    -
  Surface Roughness                X
  Settling Velocity                X
  Deposition Velocity              X
  Initial Sigmas  (cry, oz)          -
  Point, Area, Special Sources     -
  Averaging Time                   X

RECEPTORS
  x,y,z Coordinates                X
  Background Concentration         X
  Output Units                     ppm (CO)
METEOROLOGY
  Temperature
  Wind Speed                       X
  Wind Direction                   X
  Stability Class                  X
  Mixing Height                    X
  Anemometer Height                -
                   X

                   X
                   X
                   X
                   x,z

                   ppm or
                   ug/m3
                   X
                   X
                   X
                 X
                 X
                 X

                 X
                 X
                 ug/m ,
                 ppm (CO)
OPTIONS
  Highway Type


  Wind Speed


  Emission Rate



DEVELOPMENT DATA
   (See Section 3.0)


* X indicates required  input data
  At Grade,
Fill, Bridge,
    Cut
At Grade,
(elevated)
                 X
                 X
                 X
                 X
At Grade,
(elevated)
   Cut
                X
                X
                X

                X
                X
                             X
      CAL74,
      GMT 5
    GM75
    GM75
                                            X
                                            X
                X

                g/m3
                X
                X
                X
                X
                X
                X
At Grade,
 Bridge

Constant
or Height
Dependent
Constant
or Hourly
 Varying
   None
                                       -4-

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This version of the  model  was developed with the  use  of data from  two  out of

the five  field studies  used  for this  evaluation:   California Department of

Transportation's program at the  Santa  Monica site  (CAL74)  and General  Motors

Research Laboratory's program at the Milford Proving Ground (GM75).



2.2 GMLINE

    GMLINE, "A Simple Line  Source Model for Dispersion  Near  Roadways" (Chock,

1977a)  was developed  by  General  Motors  Research Laboratories  to  describe

dispersion  near  straight-line,  at-grade  highways.    Multiple   parallel  or

crossing  roadway  links  can be  simulated and  the  model allows for  a variable

emissions  height.    Empirical  relationships  from  the  GM75  field  study  are

directly  incorporated  into the  GMLINE model.   The  model  was not  designed to

treat cut-sections.

    GMLINE  simulates dispersion of  vehicle emissions by  dividing  the roadway

into  separate,  straight-line  sources,  each  with  a  uniform  emission  rate.

Downwind  concentrations  at a  receptor are calculated for each  infinite  line

source, then summed  to  obtain a total  concentration.   The model  accounts for

plume  rise due  to  heated  exhaust  and includes  a wind  speed  correction to

account for increased turbulence created by traffic wakes.



2.3 HIWAY-2

    HIWAY-2 (Petersen,  1980)  was  developed by EPA to replace the  HIWAY model

(Zimmerman  and Thompson,  1975)  for estimating roadway pollutant  impacts.  The

model  was  designed  to  determine  concentrations  at  receptors  downwind of

at-grade  roadways  and cut  sections (outside of  the  cut  only).   HIWAY-2  was

developed with data  from the GM75 field program.

    HIWAY-2 simulates dispersion by treating highway  emissions as  a series of

finite  line  sources,  each  with  a  uniform  emission  rate.   Concentrations

downwind  are  calculated  by  numerically integrating  a  Gaussian  point-source
                                       -5-

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plume along  each line segment.   The primary  differences  between HIWAY-2  and




HIWAY  are that  HIWAY-2  includes  a new  set  of  dispersion  curves  and  an




aerodynamic drag factor to  account  for  dispersion due to  vehicle  motion under




low wind speed conditions.








2.4 PAL




    The PAL model  (Petersen,  1978)  was  developed by EPA to estimate pollutant




dispersion from  point,  area  and  line sources.   It  was designed  to  simulate




dispersion  from several  types  of   roadway  geometries  including  straight  or




curved  horizontal  lines  and  straight  or curved  elevated  lines with variable




emissions  along  each  line   segment.    Model  documentation  specifies  that




treatment of  elevated  line  sources  is  appropriate for  open bridge  type road




segments  but  not   for  elevated  filled roadways.   Cut or depressed  roadway




sections  are  not treated by  PAL.   None of  the  data  sets  employed for this




evaluation were used in developing the PAL model.




    PAL  determines  concentrations  at a  receptor  due  to  a   line  source  by




numerically  integrating  the Gaussian point source equation.   Calculations are




made for a number of points along the finite line, assuming  a  linear change in




concentration  between these points.   Subsequent estimates of concentration are




made  by  including  additional points  along  the  line.    When the  difference



between  succeeding  estimates becomes  smaller  than a  prescribed  value,  the




calculations are complete.








2.5 Modifications to the Mobile Models




    Each  of  the mobile  models was  installed  and run on the EPA Univac 1100/82




computer  at  the National Computer  Center.   Although  no changes  were  made to




the  basic algorithms of any  model,  minor modifications  were necessary for the




following reasons:






                                       -6-

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    •  To enable input of data directly from the archived data sets

    •  To  allow output  of  predictions  to  disk  files  for  subsequent
       statistical  analysis

    •  To accommodate  unique features  of  each data  set (such as  hourly
       varying emissions)


The  first  two  I/O  related  changes  were  made  through   the   addition  of

subroutines MESSIN and MESSO.   Subroutine  MESSIN is called as one of the first

executable statements  in each  model.   The function  of MESSIN  is to  convert

units and  reformat all  model  input data  from EPA's Model  Evaluation Support

System  (MESS)  formats   (Computer  Sciences  Corporation,  1983)  onto  a  file

formatted  to  the  expectations of  the  respective  model.   The model-specific

formatting is done  in subroutine MODLIN,  which  is  called by  MESSIN.   MODLIN

performs  three  functions  to  create  records  of:   1)  fixed model  inputs,  2)

hourly  model  inputs  (ENTRY  MDHRLY),  and  3) hourly  model  outputs  (ENTRY

MESSO).  MESSO  is  called  hourly  by each model after concentrations  have been

calculated.   The  function  of  MESSO   is  to  convert units   of the  predicted

concentrations  and write  them to  a  MESS formatted  file.   The  observed and

predicted concentrations on the MESS files  are  in units of ppm for  CO and ppb

for SF6.

    Only the main programs for each model were modified  by TRC (i.e., existing

subroutines  were  untouched).   All  changes  were  documented  in  the  code  as

follows to allow the  respective model developers an opportunity  to  review and

approve their models before the statistics were generated:


    •  New  comment  lines  were  clearly  identified with  "TRC"  in the
       comment.

    •  New  executable   lines  contain  "TRC"   in  columns   73-75   (the
       non-executable portion of an 80 character Fortran record).

    •  For previously  executable  lines in the original GMLINE, a "comment
       out" character "C" in column 1 was included.


Additional changes made  to each model are summarized below.
                                       -7-

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CALINE3




    As  described  above,  all  coding  changes  incorporated  into CALINE3  were




documented  with  comments  in  the  source  listing.   Besides  changes  in  I/O




(subroutine  calls to  MESSIN and  MESSO),  the  CALINE3  model  was modified  to




include hourly varying rather than fixed line source emission rates.




    To  avoid any  influence  by  mixing height  on the results, a  value  of 9999




meters  was  used  for  all  runs of  the CALINE3  model.   (Actually,  any  mixing




height  greater  than  about  1000 meters  would produce  the same  results.)   It




should  also  be  noted  that  no ambient  background concentrations  were  included




in   the   model   runs.    Background  levels   were   removed   from   observed




concentrations prior to statistical analysis (see section 4.1).




GMLINE




    Major modifications to  the  GMLINE model were required in addition  to the




I/O  changes  to  allow  an  automated  treatment  of multiple  discrete  receptors




(either upwind or downwind  of  the  roadway),  hourly varying  source  emissions




and .variable buoyancy fluxes.   To handle these enhancements, several variables




were redimensioned and additional loopings were added to the original computer




code.   As a  result, the modified  model  can now handle  up to  10 line sources,




25   discrete   receptors,   and  any  number   of   hourly   (or  half-hourly)




meteorological     conditions.      Calculations     of      buoyancy     fluxes,




lane-center/receptor  distances  and wind/roadway  angle  are  now  performed each




hour by the  modified GMLINE.




    Despite  these major modifications, no  changes  were made  to the empirical




parameters  or mathematical algorithm upon  which  the original  model  was  based.




This  was  verified by  duplication of  the  example  model  runs   in  the  GMLINE




user's  guide.
                                       -8-

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HIWAY-2




    No  additional  modifications   were   necessary  for  the  HIWAY-2   model.




However, for the  CAL74  cut-section data set, the depressed highway was modeled




as  two  separate   links,  one  in  each  direction of  traffic  flow.   HIWAY-2




requires input of  a parameter specifying the "width of the top of the cut" for




depressed  roadways,  but does  not  provide guidance  on how  to treat  multiple




links  in  a  cut-section.   After  reviewing the  original  HIWAY-2  code,  it was




decided  that,  given the  width to  depth  ratio  of  the  cut,  and  to  properly




account for  emissions varying by lane, a  value  of half the cut width for each




of the two links would  be  appropriate  for input  to  the  model.   This technique




causes  the cut to  be  treated by the  model  as two  separate cuts.   The  use of




this  technique  was reviewed  and approved by  William B.  Petersen, the model




developer.   To avoid influence  by mixing height on  the  results,  a  value of




9999 meters was used for all runs of the HIWAY-2 model.








PAL




    Changes  made  to the PAL model  include subroutine calls to MESSIN and MESSO




for  I/O and the  inclusion of a  new  READ  statement for  hourly  varying  line




source   emission   rates.    Following  discussions  with  the  model  developer




(William B.  Petersen),  it was decided that  the  PAL inputs of  initial  mixing




(ayo and azo)  should   be   allowed  to  vary  hourly  as  is  done  internally




in the  HIWAY-2  model.   Two algorithms, for  at-grade and cut  sections,  define




the  initial  sigmas as  a function of wind speed.  Rather than rerun the entire




input data sequence to  the PAL model  for each hour  of data,  the  hourly sigmas




are  calculated  in a modified version of subroutine MODLIN and read  by PAL with




the hourly varying  emission rates.




    All  PAL runs  specified  the  options  for horizontal  line sources, constant




wind  speed with  height,  constant  emission  factors  (hourly varying emissions




were  permitted  by the coding  change noted  above),  and  averaging  for  each




one-hour period.  Mixing heights were  set to 9999 meters for all model runs.




                                       -9-

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3.0 DESCRIPTION OF THE DATA SETS

    Five mobile source field studies were acquired and archived for  this  model

evaluation.  The archive includes one cut-section and four at-grade data sets:


    •  CAL74  -  California  Department  of  Transportation  Santa  Monica
       Freeway - CO (cut-section)

    •  GM75   -  General   Motors   Sulfate   Dispersion   Experiment   SFS
       (at-grade)

    •  NYS76 - New York State Long Island Expressway - SFs (at-grade)

    •  NYS77 - New York State Long Island Expressway - CO (at-grade)

    •  SRI75 - SRI Highway 101 Santa Clara - CO (at-grade)


    These  5  data  sets were selected from the  13  described in a Transportation

Research Board (TRB) report (Martinez, et al, 1982) for the following reasons:


    •  Each  selected data  set  contains  enough  quality  data  to  enable
       meaningful  statistics  to be  generated  for 1-hour  and,   in some
       cases, 8-hour averaging periods.

    •  Appropriate data were  collected to provide the required  inputs  to
       each of the mobile models.

    •  The variety of data bases chosen allows model  evaluation  for both
       at-grade and cut-section roadways and for emissions of both CO and
       SF6 tracer.

    •  Data  collected in  each  study enable model  evaluation over a wide
       range of meteorological conditions.
    Originally, most  of  the data needed for the mobile model evaluation was to

have  come  from  the  report  and  associated  archive  tape  prepared  for  the

Transportation  Research  Board  (Martinez  et al., 1982).  However,  in order to

produce complete data sets which satisfy all input  requirements  of  the models,

information had to be gathered from several additional  sources.   For example,

emissions for each CO data  set were calculated using EPA's  MOBILE2 or MOBILES

emissions models  based on  traffic information at  each site.   Data were only
                                      -11-

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archived for hours  containing  complete information (source data,  meteorology,




and air quality)  needed to run and evaluate  each  model.   Data sources for all




parameters archived or  used as  input  to the  emissions  models are  documented




for each data set in the sections that follow.








3.1 California Department of Transportation - Santa Monica Freeway (1974)




    The  archive  for  the  CAL74  Santa  Monica Freeway  cut-section  data  set




includes  795  hours of  data  collected  from  April  through  July, 1974.   The




monitoring   configuration   is   shown   in   Figure   3-1.    Carbon   monoxide




concentrations measured  at  the six ground-level (1.5 meters)  locations outside




the cut-section were archived.   Ambient wind speed and wind direction measured




at the  center  of the  median (6.7  meters  above the top of  the  cut section and




14.0 meters  above street level)  were  archived to  provide the best  available




representation of transport  above the cut.  Hourly stabilities were determined




from  cloud  cover and  ceiling height  observed at  Los  Angeles  International




Airport  (about 15  kilometers  from the site)  and on-site  wind speeds measured




at 6.7 meters above the  top of the cut section.




    As  described  in  Bemis, et  al  (1977),  the Santa  Monica Freeway  site is




removed  from  other  localized pollutant  sources.   There are  no  additional




freeways  or main  surface  streets in the immediate area.   The  site is  located




in a mixed  single-double story residential area, with homes ranging from 20 to




30 feet in  height.



    Data from  the CAL74  Santa Monica Freeway  mobile  source study were gathered




from  several sources.   Sources of all parameters archived  or used  in archiving




are given below:
                                      -12-

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                                                              LEGEND

                                                         D   CO PROBE

                                                         X   TEMPERATURE

                                                         O   ANEMOMETER
                                                          NOT  TO  SCALE
                ,13.4m
                              Ql4-0m
|-45.7-|-45.7-| - 12.5
                                                        4]   [si    [6] 1.5m
                                                12.5 - 1-45 . 7^45 .
                              DISTANCE (m)
                      TOWER
                                                        14115(161
Figure 3-1.  Monitoring configuration  for  the  California Department of
             Transportation CO data base  (CAL74-CO).
                             -13-

-------
1.  Martinez, et al (1982)

   •  Road geometry
   •  Anemometer location
      - located at  center of median,  6.7 m  above  grade and  14.0  m above
        street level
   •  CO receptor coordinates
   •  Traffic hot/cold mode percentages
      - 6.0% cold start and 2.0% hot start mode
   •  Vehicle type distribution
      - 77.0%  light  duty  vehicles,  11.6% light  duty trucks,  1.4% medium
        duty  trucks,  4.5% heavy  duty  gas trucks,  4.5%  heavy  duty diesel
        trucks, and 1.0% motorcycles
   •  Wind speed (from TRB computer tape)
   •  Wind direction (from TRB computer tape)
   •  Temperature (from TRB computer tape)
      - measured at 13.4 m above grade at south edge of cut section
   •  CO concentration (from TRB computer tape)
   •  Sigma theta (from TRB computer tape)
      - measured at the anemometer location described above
   •  Traffic volume (from TRB computer tape)
   *  Traffic speed (from TRB computer tape)
   *  Ceiling height (from TRB computer tape)
      - observed at Los Angeles International  Airport NWS station
   •  Cloud cover (from TRB computer tape)
      - observed at Los Angeles International  Airport NWS station
2. Benson (1979)

   •  Surface roughness (100 cm)
   •  Source length (5 km)

3. EPA (1977)

   •  Stability  (using  wind  speed,  cloud  cover,  ceiling  height,
      sampling   time   and   geographic   location),   from   CRSTER
      preprocessor program.

4. Bemis, et al  (1977)

   •  Roadway angle (80° 12' 53" east of north)
   •  Number of  lanes  (six  in each direction; modeled as two links)
                                   -14-

-------
    5.  Calculated using MOBILE2  Emissions  Model  (EPA,  1981)

       •  CO Emission Factors
          MOBILES could not be  used because  it does not  have an  option
          for California  emission factors.   The  following MOBILE2  input
          options were used:

                Identical  speed  for all  vehicle  types, varying by  hour
             -  User supplied  single VMT mix (from 1)
             -  MOBILE2 default  registration and mileage  distributions
             -  MOBILE2 default  exhaust  emission rates
             -  No I/M program
             -  No special correction factors
                California emission factors
             -  1974 calendar  year
             -  Speeds, temperatures, and  hot/cold mode
                percentages as given in  TRB report

          MOBILE2  emission factors  (grams/mile)  were  generated  for  each
          combination of speed  (5  to 55 mph) and ambient  temperature (20
          to 95°F).   They were  then matched with  the  traffic speed (to
          the  nearest  mph  for  each  direction   of  flow)   and   ambient
          temperature  (rounded  to the  nearest  5°F) for each hour  during
          which data were  collected.

       •  CO Emission Rates
          Emission  rates  were obtained  by multiplying the traffic  volume
          (for each  direction of  flow)  by the  MOBILE2  emission  factor
          (described above) for each hour of data  collection.   Emission
          rate units are grams/meter/second.
3.2 General Motors (1975)

    The archive  for  the GM75  tracer data  set  contains  SF6  measurements  for

62  periods  of  30 minutes  each  from an  at-grade  oval  test  track at the  GM

Proving Ground  in Milford,  Michigan.   The  site  diagram  is  shown in  Figure

3-2.  Tracer was  released  from seven or eight pickup trucks distributed evenly

among  352  vehicles  traveling  on  the   track.   Experiments  were  run  during

October, 1975.

    As described in Cadle,  et al (1976), the GM Proving Ground site  is located

in a  rural  area,  surrounded  by rolling  hills and light woods.   The site itself

is essentially flat and open.  The  nearest  major highways are located  7  km to

the south and 6 km to the west.
                                      -15-

-------
                                                                LEGEND
                                                           Q  SFg PROBE
                                                           X  TEMPERATURE
                                                           O  ANEMOMETER
                                                             NOT TO  SCALE
    14. 5 m
)4.5 m
                                                                    f/l 0.5m
   h28.1-|L9(&8J11.8|6.8|3.3|11.2|-15.0-|—20.0— |
                    -50.0-
                              DISTANCE  (m)
Figure 3-2.  Monitoring  configuration for the General  Motors SFfi data  base
             (GM75-SF6).                                         °
                                    -16-

-------
    The GM  archive contains half-hour  averages of  SF6  concentration for  the

seven ground-level  (0.5 meter),  non-median monitoring locations.   In addition,

wind  speed,  wind  direction,  and  temperature  at  a  height  of  approximately

4.5 meters were archived  from  the  tower located 30 meters  upwind of the track

(as determined for  each  sampling period) to  represent ambient  plume transport

conditions  not  affected  by  the  roadway.   The  archive  also  includes  SF6

emission rates for each sampling period.

    Data from  the General  Motors  Sulfate  Dispersion Experiment  were obtained

from  several  sources.   Hourly  averages  contained  on   the   Transportation

Research Board  archive tape were  not  used in order to maintain  the integrity

of  the  original   30  minute  averaging  periods.   Sources  of  all  parameters

archived for the mobile model evaluation are given below:


    1. Chock (1977b)

       •  Road geometry
       •  Number of lanes (2 in each travel direction)
       •  Traffic  volume   (equivalent   of  5462   vehicles/hour,  equally
          divided between 4 lanes)
       •  Source length (5000 m)
       •  Anemometer location (30 upwind of road,  at a height of 4.5 m)

    2. Chock (1977a)

       •  Buoyancy   flux   (0.052  mVsec3   for  traffic   volume   stated
          above)

    3. Chock (1977c)

       •  Stability  (local  Colder stability used in  the model  runs; also
          archived  Turner  stability  derived  from  Detroit  Metropolitan
          Airport NWS data)
       •  Surface  roughness (3 cm)

    4. Cadle, et al  (1976)

          SFS emission rates
          SFS emission factors
          SF6 receptor locations
          SF6 ambient concentrations
          Wind direction
                                      -17-

-------
       •  Wind speed
       •  Temperature (measured 30 m upwind of road,  at a height of 4.5 m)
       •  Ambient pressure
3.3 New York State Long Island Expressway - CO (1977)

    A total of 303  hours  of data were archived from an at-grade site along the

Long  Island Expressway in New  York State.   The  NYS77  study  was  conducted

during April  and May,  1977.   Figure 3-3  shows  the site arrangement  for this

study.   Carbon  monoxide   concentrations  from  three  ground-level  (2  meter

height),  non-median  monitor  locations  were  included  in  the  archive.   Wind

speed and wind direction  recorded at a height of  2 meters  on a tower located

37  meters  north   of  the  road  edge  were  archived  to  represent  ambient

plume-level transport  conditions.   Ambient temperature,  measured  at  a height

of 4 meters, 21.5 south of the road edge, was also archived.

    As  described in Rao,   et  al  (1978),  the  Long Island  Expressway  site  is

situated in a  relatively  flat and open area, surrounded by sod farms.  A small

grove of trees is  located 200 meters to the  south.   The nearest  major roads

are located approximately 0.5 and 1.5 km to the west of the site.

    Data from  the  NYS77 Long  Island Expressway CO field study were gathered

from several sources.  Sources of all parameters archived are given below:


    1. Martinez,  et al (1982)

       •  Traffic volumes  and speeds  (averages  based on time  of  day and
          day of week)
       •  Traffic hot/cold start mode percentages
          -  0% hot and 0% cold mode.
       •  Vehicle  type distribution  (81%  light  duty vehicles, 9% light
          duty trucks,  6.5% heavy  duty gas  trucks,  and 3.5%  heavy duty
          diesel trucks)
       •  Wind speed  (from TRB computer tape)
                                      -18-

-------
                                                             LEGEND

                                                        D  CO PROBE

                                                        X  TEMPERATURE

                                                        O  ANEMOMETER
                                                          NOT  TO  SCALE
4m •
2m-
s.
I
      16 - —
                                |-13.5H-20. 0-|-13. 5-|l.5|-20.0-|
                               DISTANCE  (m)
         O
                                                    X
   Figure  3-3.   Monitoring  configuration for the New York CO data base
                (NYS77-CO).
                                   -19-

-------
   •  Wind direction (from TRB computer tape)
   •  Temperature (from TRB computer tape)
      -  measured at 4 m height,  21.5 m south  of road edge
   •  Stability (from TRB computer tape)
      -  Turner stability using data from Islip Airport NWS station
   •  CO concentration (from TRB computer tape)

2. Rao, et al (1979)

   •  Road angle (90° east of north)
   •  Number of  lanes  (3 in  each travel  direction;  modeled as  two
      links)
   •  Road geometry
   •  Source length (1500 m)
   •  Anemometer location (37 m north of road  edge at a height of 2 m)
   •  CO receptor coordinates
   •  Supplemental   wind   speed,    wind   direction,   temperature,
      stability, and CO  concentration for hours  not  included on  the
      TRB tape

3. Rao, et al (1978)

   •  Site description (used to determine surface roughness of 10 cm)

4. Calculated using the MOBILES Emissions Model (EPA, 1984)

   •  CO Emission factors
         Determined  using  available  inputs   from   above   and  the
         following MOBILES options:

         -  MOBILES tampering rates
         -  Identical speed for all vehicle types, varying by hour
         -  User supplied single VMT mix (from TRB report)
         -  MOBILES default registration and mileage distributions
         -  MOBILES default exhaust emission rates
         -  No I/M program
         -  No special correction factors
         —  No anti-tampering program
         -  Low altitude emission factors
         -  1977 calendar year
         -  Speeds,  temperatures,  and hot/cold  mode  percentages as
            given in TRB report

      MOBILES  emission factors  (grams/mile)  were generated  for  each
      combination of  speed (5 to 55  mph)  and  ambient temperature (20
      to  95°F).   They  were  then matched  with  the  average  traffic
      speed  (to  the nearest mph) for  each direction of flow  (by  hour
      and  day  of  week)  and  ambient  temperature  (rounded  to  the
      nearest 5°F)  for each hour during which  data were collected.

   •  CO Emission rates
      Emission  rates  were obtained by multiplying  the traffic volume
       (for  each  direction of  flow)  by  the   MOBILES  emission factor
       (described  above)  for each  hour of  data collection.  Emission
       rate units are grams/meter/second.
                                  -20-

-------
3.4 New York State Long Island Expressway - SF6(1976)

    The  NYS77  CO  program was  preceded  by  NYS76,   a   series  of  SF6  tracer

experiments in October  and November 1976.  From these  experiments,  a total of

21  hours of  complete  data  were  available.    SFS  concentrations  from  eight

ground  level   (2  meter)  non-median  monitor  locations  were  included in  the

archive.  These locations are shown in Figure 3-4.

    Data from  the  1976  tracer study were gathered from two references as given

below:


    1. Rao, et al (1979)

       •  Road angle (90° east of north)
       •  Number  of lanes  (3 in  each travel  direction;  modeled as two
          links)
       •  Road geometry
       •  Source length (1160 m)
       •  Anemometer location (center of median at 8 m height)
       •  Wind speed
       •  Wind direction
       •  Turner stability
       •  Local stability (used in model runs)
       •  SF6   emission  rates  (determined  by  dividing  hourly  emissions
          (g)   by  source length  (1160  m) and then by time  length of run
          (3600 sec))
       •  SF6  ambient concentrations
       •  SF6  receptor coordinates


    2. Rao, et al (1978)

       •  Site description (used to determine surface roughness of 10 cm)



3.5 SRI Highway 101 (1975)

    The  archived  SRI75  data  set  is made  up  of 45 hours of  data collected on

six days in January and February, 1975  at  an  at-grade  site (see Figure  3-5)

along Highway  101 in Santa Clara, California.  Carbon  monoxide concentrations

at 22 non-median  receptor locations (18 at a height  of one meter and 4 at  a

height of three meters) were archived.  Ambient wind speed and wind direction,

measured at a  height of 3.8 meters on towers  located 30.5 meters  upwind  of  the


                                      -21-

-------
                                                             LEGEND

                                                         O  ANEMOMETER

                                                         D  SF  PROBE
                                                           NOT TO SCALE
8m—
2m—
                   O
— 20.
                                 . 5-(—20. 0-|-13. 5^. 5J-20. 0-1-16. 0-|— 18.0—
                                   DISTANCE  (m)
            2     |3|
                   O
     Figure 3-4.  Monitoring configuration for the New York SFfi data base
                 (NYS76-SFg).
                                        -22-

-------
                                                             LEGEND

                                                         D  CO  PROBE

                                                         X  TEMPERATURE

                                                         O  ANEMOMETER
                                                          NOT TO SCALE
        •TOWER
7-5- Ch
3.8- O
3.0-
i.o-
^**^ \
H]D r
HiE f-

~\ii\
-$7\

                                                       /
    	19.8	[-10.7-]-
                      •36.6-
.L-iQ.7-1	19.8	'
                                DISTANCE (m)
            TOWER
                                                      4   3   2   1
                                              TOWER
                    -110m-
                                     .110m.
Figure 3-5.
Monitoring configuration for the SRI CO data base  (SRI75-CO).
CO probes shown in the lower diagram are at a height of 1 meter.
                                    -23-

-------
road edge, were  included  in  the archive to provide the  best  available ambient

representation  of  transport   at  plume   height.    Stability  category   was

determined using on-site  radiation  measurements,  along with the wind  speed at

a height of 7.5 meters, 30.5  meters  upwind of the road edge.

    As described in Dabberdt,  et al (1981) The Highway  101  site is surrounded

by flat,  low-cut grass fields,  with two 7  meter  high earth mounds  350  meters

to the  east.   The nearest major roadways  are located 500 meters  to  the  west

and east of the site.

    Data from  the  SRI75  Highway 101 study were gathered from several sources.

Sources of all parameters archived or used in archiving are given below:


    1. Wolf, et al (1981)

       •  Road angle (110.6°  east of north)
       •  Number of lanes (3  in each travel direction)
       •  Road geometry
       •  Anemometer  location   (measured  at 3.8  m  height, either  30.5  m
          north or south of road edge, depending on upwind direction)
       •  CO receptor  coordinates
       •  Wind speed (from associated FHWA computer tape)
       •  Wind direction  (from associated FHWA computer tape)
       •  Temperature  (from associated FHWA computer tape)
          -  measured  at  a height of 3.8 m, either 30 m north or south of
             road edge, depending on upwind direction
       •  CO concentration (from associated FHWA computer tape)
       •  Sigma theta  (from associated FHWA computer tape)
          -  measured  at  same location as wind speed and direction
       •  Solar radiation   (from associated FHWA computer tape)
       •  Traffic volume     (from associated FHWA computer tape)
          -  total volume per lane per hour
       •  Traffic speed      (from associated FHWA computer tape)
          -  speed per lane per hour

    2. Benson  (1979)

       •  Traffic hot/cold mode percentages {20%  cold start  mode,  27% hot
          start mode)
       •  Vehicle  type  distribution (81.5%  light duty  vehicles,  12.1%
          light  duty  trucks,  1.3% medium duty trucks, 4.5% heavy duty gas
          trucks,  0.5% heavy duty diesel trucks,  and  0.1% motorcycles)
       •  Surface  roughness  (10 cm)
       •  Source length  (6000 m)

    3. Calculated  using  the method  described by Ludwig and Dabberdt  (1976)
                                      -24-

-------
   •  Stability (using solar  radiation,  7.5 m height wind  speed  from
      FHWA tape,  and calculated sun angle)

4.  Calculated using the MOBILE2 Emissions Model (EPA,  1981)

   • CO Emission Factors
         MOBILE3 could not be  used because it does not  have  an option for
         California emission factors.   The following MOBILE2  input  options
         were used:

      -  Identical speed  for all vehicle types, varying by hour
      -  User supplied single VMT mix  (from CALINE3 User's Guide)
      -  MOBILE2 default  registration  and mileage distribution
      -  MOBILE2 default  exhaust emission rates
      -  No I/M program
      -  California emission factors
      -  1975 calendar year
      -  Speeds, temperatures,  and hot/cold mode  percentages as given  by
         the FHWA tape and CALINE3 User's Guide

   MOBILE2   emission  factors   (grams/mile)   were   generated   for   each
   combination of  speed  (5 to  55 mph)  and  ambient  temperature  (20  to
   95°F).  They  were then matched with the traffic speed  (to the  nearest
   mph) for each lane and ambient temperature  (rounded to the nearest 5°F)
   for each hour during which data were collected.

•  CO Emission Rates
      Emission rates were  obtained by multiplying the traffic  volume (for
      each lane) by  the  MOBILE2 emission factor (described above) for each
      hour of data collection.  Emission rate units are grams/meter/second.
                                  -25-

-------
4.0 STATISTICS APPROACH




    The 1980  AMS Woods Hole  Workshop on  model  performance evaluation  (Fox,




1981)  recommended a  comprehensive list of performance measures  and statistics




for evaluating air quality  models.   The workshop recommended that  performance




evaluations be based  on  comparisons of the full set of observed-predicted data




pairs, of the highest observed and predicted concentrations  per  event  (e.g.,  1




or  8  hour time  period),  and of  the highest  "N"  values  (unpaired  in time or




space).  In addition, comparisons of observed  and predicted  concentrations for




data   subsets   representing   individual   monitoring   stations  or  selected




meteorological conditions were recommended.




    The  statistical  analyses performed  for the  mobile  source  model  category




are  based upon  the  recommendations of the  1980 AMS  Workshop,  together  with




insights  gained  through evaluations  of  rural  (Londergan, et al,  1982),  urban




(Londergan,  et  al,  1983),  and  complex terrain (Wackter and Londergan,  1984)




model  categories.   The  intent  of  the statistical  analysis  is to provide  a




thorough  assessment of model  performance,  avoiding redundant or uninformative




calculations.








4.1 Pre-Analysis Data Handling Procedures




    A  number  of  issues concerning  analysis  procedures  can  be  addressed  on  a




generic  basis,   without  regard for the differences  among  the selected  data



sets.   These  issues   include   the  selection  of  monitors;   estimation  of




background concentrations;  averaging times; use of a  concentration threshold;




and choice of meteorological subsets.  Additional analysis issues must also be



discussed for the cut-section data set.








    Monitor Selection.  For regulatory applications,  ambient concentrations at




ground level  (or  "breathing  height") are  the primary  concern.   Likewise,






                                      -27-

-------
monitors located in  the  median of limit-access highways are not of concern for




the current  evaluation.   For  the  present  study,  analysis  was restricted  to




near-ground monitors, and excluded monitors in the median of a divided highway.




    The SRI  - at grade  monitoring program utilized an  array  of 22  monitors




which were  symmetrically located  on  both  sides  of  the  highway.   Because the




models  simulate  transport  and dispersion  identically  on both  sides of  the




roadway, the  11  sets of  "twin" stations on opposite  sides of the roadway were




treated as  single  stations for  the  "by   station"  subsets.    This  enabled  a




reduction  in  the   volume   of  statistics  generated  without any   loss  of




information, as  well as  an increase  in the population  sample  size  for  those




statistics which were generated.








    Background  Concentrations.   Each  of  the measurement  programs  included




monitors  on both  sides  of the  roadway.   Background  CO concentrations  were




assumed to be  evenly distributed over the  study area and were estimated by the




lowest  measured  concentration   in   the   network   each   hour.    Observed




concentration  values  were  calculated as  the measured  minus  the  background




concentration.   A background  level of  zero ppb was  assumed for the  SF6  data




sets.








    Averaging  Times.   National  ambient   air  quality   standards  for  carbon




monoxide  exist for  1-hour  and 8-hour  averaging  times.   The basic prediction




unit  for  the models under  consideration is one hour.  The selected data sets,




except GM75, provide one-hour  measurements  of ambient concentrations, traffic,
                                      -28-

-------
and  meteorological  variables.   The  GM75  data  set is  made  up of  half-hour

measurements.   Statistical  analyses were performed for  half-hour  (GM75  data

set  only),  1-hour (all other  data sets) and 8-hour averaging  periods (CAL74

and NYS77-CO data sets  only).   A minimum of  6  hours of data were  required to

estimate the 8-hour averages.   Background  values for the 8-hour data sets were

determined by  calculating the  average  of  the  8 individual  hourly background

values.



    Threshold.   Near-zero  observed and predicted concentration  values  are not

of great interest for testing model performance.   For  the mobile  source  data

sets, monitors located  upwind  of the roadway generally  show  negligible impact

from roadway emissions.   To eliminate these  data  points from  the statistical

analyses, a  screening threshold  of 0.5 ppm for CO, after background removal,

was  used  for  the  one-hour  averaging  period.    This   value   represents  a

characteristic  measurement  uncertainty  as  determined  for  an  experimental

program in Texas  (Bullin,  et al,  1978).  When both  the  observed and predicted

CO  concentrations at a  monitor were  below this threshold  for a  given hour,

that data pair was  eliminated from the data  set.   No  threshold  screening was

performed for 8-hour CO averages or for SF6  concentrations.



    Meteorological  Subsets.   The  key meteorological variables  for estimating

pollutant concentrations  within 25  m of a  highway are wind  speed and  wind

angle  (wind  direction,  relative  to the highway direction).   As  the distance

from   the  roadway   increases,   atmospheric  stability   becomes  increasingly

important.   Meteorological  subsets  selected  for  statistical  analysis  are

summarized below:
    •  Wind Speed (u):  u < 2 m/s
                        2 < u < 4 m/s
                        u > 4 m/s

                                      -29-

-------
    •   Wind Angle (0):   G <  20°  (near parallel)
                         20°  <  0  <  60° (oblique)
                         0 >  60°  (near perpendicular)

    •  Stability:   unstable (Class  A,  B,  C)
                   neutral (Class D)
                   stable (Class  E, F, G)


    Cut-Section Data  Set  (CAL74).   Review  of  model  provisions  in  HIWAY-2

suggests that predictions  are  only meaningful for  receptor locations  outside

of the cut-section.  GMLINE  and  PAL V7ere not developed to  treat cut-sections,

although PAL does  allow the user to vary initial mixing on  input.  CALINE3 is

the  only  model   which   allows   receptors   to   be   located within  the  cut.

Conseguently, only the six ground-level  monitors outside  of the  cut  were used

to  test model  performance  for  the   CAL74  Santa  Monica  data  set.    The  CO

measurements were  not taken  simultaneously  at all  of  the  monitoring  stations

for  all  measurement  periods.   To use  any hour  for  statistical analysis,  CO

concentrations at  both upwind  (to  estimate  background) and  downwind  locations

outside of the cut must have  been available.



4.2 Statistical Performance Measures

    The  AMS  workshop report  recommended  two  somewhat  different  lists  of

performance  measures  for  comparing  model  predictions  with  observed  air

guality,  one appropriate  for  data  sets representing pairs  of observed  and

predicted  values,  the other appropriate for unpaired data  sets.  Paired data

sets provide  a  means  for assessing how  well  a  model  predicts on an  event-by-

event  basis,  while unpaired  sets  do  not.   Table 4-1  summarizes  the basic list

of   performance   measures,  and   the  statistical   methods   recommended  for

establishing confidence  limits on  each measure.

     For  data sets paired in  time,  statistical analyses  based on  residuals

(i.e.,  the differences  between each  pair of  observed and predicted values) are
                                      -30-

-------
                                    TABLE 4-1

                 STATISTICAL ESTIMATORS AND BASIS FOR CONFIDENCE
                          LIMITS ON PERFORMANCE MEASURES
Performance
Measure
Bias

Woise/Scatter



Correlation

Basis for Confidence Interval
Estimator
Average
Median
Variance
Gross
variability
Average
absolute
residual
Pearson
correlation
coefficient
Paired Comparison
One sample "t," with
adjustment for serial
correlation
Wilcoxon match pair
Chi-squared test on
variance of residuals
None

None
Fisher "z"

Unpaired Comparison
Two sample "t"
Mann-Whitney
F test on variance
ratio
Not applicable

Not applicable
Not applicable

Frequency
distribution
comparison
Maximum
difference
between two
cumulative
distribution
functions
Not Applicable
Kolmogorov-Smi rnov
(K-S) test on f (obs.)
vs. f (pred.)
                                      -31-

-------
appropriate for  measuring model performance.   If the  time  pairing for  these

data sets  is  ignored,  however,  it  is also possible to assess model performance

(in aggregate) by comparing the features  of the composite set of  all  observed

values  to  those of  the  predicted  values.    Consequently,  both  paired  and

unpaired  comparisons  were  recommended by  the  AMS  workshop for  these  data

sets.  Data sets  representing  comparisons of the highest values, regardless of

time or  space, provide  no  basis  for  paired  analysis.   For  these sets  only

unpaired comparisons were performed.

    For paired comparisons,  as noted above, the  performance  measures  are  based

on  an  analysis of  residuals.   Model  bias  is  indicated by  the  average and/or

the  median  residual,  with  a  value  of  zero  representing  no  bias.    The

characteristic  magnitude of  the   residuals  is  an  indicator  of  the  scatter

between  observed and  predicted  values  on  an  event-by-event  basis.   Three

measures of noise or scatter were computed:


    •  Variance   1      >   (d, - d)2
                 N-l    Z—<
    •  Gross variability
                          N
       Average absolute residual  1     \   |dj
                                  N     *  1
where  di  is  the  residual  (observed minus  predicted) for  data pair  i,  d is

the  average  residual,  and N is  the  number of data pairs.   The  correlation of

paired  observed and predicted  values is  measured by  the  Pearson correlation

coefficient.

     For  unpaired  comparisons,  the  list  of  performance measures  is  somewhat

shorter.   Model bias is  indicated by the difference between the  average  (or


                                      -32-

-------
median) observed  value and  the  average (median) predicted value.  A  ratio of




the variances  of  the  observed  and predicted  values  is  provided to  indicate




whether  the  distribution of  values  in  the  two data  sets  is  comparable.




Similarly, the frequency distribution of observed values  is compared with that




for predicted values.




    Standard statistical methods  were  used to  estimate confidence limits  for




each  of the  performance  measures.  Discussion  of the statistical  procedures




may be  found  in  most  statistics  textbooks.   For  parametric procedures,  the




reader  is   referred  to Snedecor  and Cochran  (1967),  while  for  nonparametric




procedures Hollander and Wolfe (1973) provide an appropriate description.




    For  paired comparisons,  the  confidence  interval  on  the  average  residual




was estimated  using  the  one-sample t test.  This  parametric  test incorporates




the assumption that  the  residuals follow a normal  distribution,  but  for large




M  departures  from normality  are not critical.   Serial correlation can affect




results,  since the  number of "independent events" could be  overestimated and




the calculated variance  could  understate  the magnitude  of the  actual random




error  component.   However,  for  the mobile data  sets,  this  effect should be




minimized since much of the data  is not continuous in time.




    An   analogous  nonparametric   indicator  of  model   bias   is  the  median




residual.   The statistical method for  estimating a confidence  interval on the




median   residual   is  provided   by  the   Wilcoxon matched-pairs   test.   Wo




straightforward method of adjusting the confidence  intervals  from the  Wilcoxon




test for serial correlation has been identified.
                                      -33-

-------
    A confidence  interval  for the  variance of  the  residuals  was  calculated




using a chi-sguared test.   No adjustment was made  for  serial  correlation.   No




standard method is available for  estimating confidence intervals for  the  gross




variability  or   average   absolute  deviation   measures.    For   the  Pearson




correlation coefficient, the Fisher z test provided a method of  estimating  the




confidence interval.




    Comparison of two cumulative  distribution functions was accomplished  using




the  Kolmogorov-Smirnov  (K-S)  test.   For  this  test,  the  two  distribution




functions are  compared across the  full  range  of concentration (or  residual)




values,   and the  maximum  frequency  difference  between  the  two functions  is




identified.




    For unpaired  comparisons,  two  bias  measures were computed.   The average of




the  observed  values  was compared  with  the  average of  the predicted  values.




The  confidence  interval on  the difference of the averages was estimated with a




two-sample  t  test.    The  median  difference  was  also   computed,   and  the




confidence interval was estimated using the Mann-Whitney nonparametric test.




    The  variance  of  observed  values  was  compared  with  the  variance  of




predicted values  for  unpaired data sets.  The performance measure  is the ratio




of  the  variances; the  F  test provides  confidence  limits  on the   ratio.   The




frequency distribution  comparison  for  unpaired data sets provided a measure of




the  difference between the observed  and  predicted distribution functions.  The




K-S  test  was  again used to assess  the  statistical significance of the maximum




frequency difference.









4.3  Data Sets For Statistical Analysis




     Twenty  basic data  sets  were compiled  as  a  product  of the data from the




five field  programs run through each of  the four  mobile  models.  Each of these




basic  data  sets  were screened  and  processed  for missing  data,   threshold,






                                      -34-

-------
background and averaging  times  as  described above and then sorted into subsets

by station  and meteorological  conditions.   Various unpaired  and paired  data

groups were then developed for subsequent statistical analysis.

    The statistical measures  generated  for the mobile source  model  evaluation

are  very  similar to  those utilized  for the urban  and  complex  terrain model

evaluations, except for some of the selected data sets the  amount of available

experimental  data  limited  the  extent  of  the analyses.    Statistical  measures

are  discussed  separately  for  the unpaired  (N highest  values)  and paired  data

sets.



    Unpaired  (M  Highest Values).  The  array  of  statistical analyses performed

for  the  N  highest  observed  and  predicted  values,  regardless of  time  or

location,   are summarized  in Table  4-2.   The  principal  change  from  earlier

model  categories  concerns  the  choice  of  N,  the  number  of  peak  values

analyzed.    Previously,  this  number  had been  fixed at  25.   In  light  of the

small number  of  measurement hours  available  for three of  the data  sets, the

number of  peak values analyzed in this  study was reduced to 25  percent of the

available  hours  or 25  hours, whichever was  less.   (Otherwise,  the data set

would not  accurately  reflect  the "top  end" of the frequency distribution.  )  To

reduce  spatial correlation,  not more  than  one  observed  and predicted value

from  any  hour  was  chosen.  This  prevented  the high-N  analysis  from being

unduly  influenced  by one  or  two hours of overall  high  concentrations.   The

confidence  intervals  for  these  data sets are based upon  a two-sample t-test.

These confidence intervals  should  be viewed with caution,  since  the N highest

values  are  selected  based on  their rank  within the distribution.   The  data

set, therefore, does not represent a random sample of N independent values.

     As noted previously,  data from "twin"  stations  from  the SRI data set were

combined  for the subset analysis by  station.   Meteorological subsets  for the

one-hour data were also defined in an earlier discussion.
                                      -35-

-------
















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    Analysis of the "highest N"  8-hour average values was only  undertaken for




the CAL74  cut-section and NYS77  CO data  sets.   For the  New York  data  set,




fewer than  100 8-hour periods were available,  so the selected N value  was 25




percent  of  the   available   number,   rather   than  25.    Statistics   for




meteorological subsets were not computed for the 8-hour  data  sets.








    Paired in Time and/or Location




    Statistical analyses  undertaken for  paired  data sets  are  summarized  in




Table  4-3.   The first  two columns represent  subsets of  the  full basic  data




set.  For column one,  the highest observed and  predicted  values are  selected




for each  time period (paired  in  time  but not necessarily in  space),  and  then




analyzed  statistically  as  a  group.    Similarly  the   highest   observed  and




predicted  values  at  each station  for  the  period of  record  (data paired by




station but not necessarily  in time)  are grouped  for statistical analysis in




column  two.   The  full  basic  data  set  is represented  in  column three  by the




group of all observed and predicted values paired for each time  period at  each




station.  Fully paired comparisions are also made for observed and  predicted




concentrations at each  station (column four) and for each meteorological  data




subset  (column five).
                                      -37-

-------
                                    TABLE 4-3

             ARRAY OF PERFORMANCE MEASURES AND STATISTICS CALCULATED
               FOR THE MOBILE DATA SETS PAIRED IN TIME OR LOCATION
Highest Highest All Data All Events Meteorological
per event per station paired in at each Subsets of
paired in paired by time and station events paired
time location location paired in in time
time and location
Number of events
Averaged observed
Average Predicted
Difference of Averages
Average difference
Fraction Co > Cp
Characteristics
Discrepancies
ad
RMSE
AAD
X
X
X
X (C.I.)*
X (C.I.)
X


X (C.I.)
X
X
X
X
X
-
X (C.I.)
X


X (C.I.)
X
X
X
X
X
X (C.I.)
X (C.I.)
X


X (C.I.)
X
X
X
X
X
X (C.I.)
X (C.I.)
X


X (C.I.)
X
X
X
X
X
X
X
X


X
X
X



(C.I.)
(C.I.)



(C.I.)


Correlation coefficient
Pearson R
Variance comparison
Maximum frequency
difference
X (C.I.)
X (C.I.)
X (C.I.)

X (C.I.)
-
-

X (C.I.)
X(C.I.)
X(C.I. )

X (C.I.)
X (C.I.)
X (C.I.)

X
X
X

(C.I.)
(C.I.)
(C.I.)

* C.I.  = 95% confidence interval
                                      -38-

-------
5.0 MODEL PERFORMANCE RESULTS




    Statistics comparing  observed and predicted concentrations were  generated




for each  combination of  the  four mobile  source models  and  five  data  bases




using the approach  described  in Section 4.0.   Statistics were produced for the




"N" highest  observed and predicted  values, highest  concentrations  by  event,




highest   concentrations  by    station,   and   all    observed  and   predicted




concentrations paired  in space  and  time.   A  summary of  results  for each  of




these  data  groupings  is  presented  in  this  section.   Complete  tables  of




statistics,  including  breakdowns  by station  and meteorological  subset,  are




presented in Appendices A through D.








5.1 Statistics for "N" Highest Values




    One-Hour




    Summary statistics  for  the  "N" highest observed  and  "N"  highest  predicted




1-hour average CO  and  SFs concentrations are  presented  in Table  5-1  for each




model  and data base.   Because  some of  the data bases  are  fairly  small, the




value of  "N" for each  data  set  was determined by selecting  either  the highest




25  percent  or highest  25 concentration values,  whichever was less.   Caution




should  be exercised  when  interpreting  the meaning  of   statistics  generated




using the smaller data sets.




    The number of events  used to calculate statistics for each model  and data




base  is  given in the  first column of  Table 5-1.  Averages of the  "N" highest




observed  and predicted values for  each  data set are  listed in columns two and




three, respectively.   The first performance measure,  presented in column four,




is  the difference  between the  two averages.   A positive value  implies  model




underprediction.• In parentheses adjacent  to the calculated differences are 95




percent confidence  intervals,  determined by using  the  two-sample  Student's t




test.  Confidence  limits  which contain zero imply that there is no significant






                                      -39-

-------





















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-40-

-------
difference  between modeled  and  observed  concentrations  at  the  95  percent




confidence level.




    Results  for   the  "difference  of  averages"  comparison,   as   displayed




graphically in  Figure 5-1,  show  differences both  between models and  between




data bases.  The  best model  performance occurs with the two tracer data bases,




GM75 and NYS76.   Little bias is observed  for these two data bases  except with




the PAL and HIWAY-2 models which show significant overpredictions for GM75 and




NYS76,  respectively.   The  least  bias  and  smallest  confidence  bounds  are




exhibited  by  GMLINE.   In  general,  much poorer model  performance is  exhibited




for the  three CO  data bases.   GMLINE  performed best  for both  at-grade  data




sets  (NYS77  and SRI75) while  PAL performed worst.  The  cut-section  data base




(CAL74) appears to  be an  exception,  with  large  underpredictions by three  of




the models and unbiased performance by PAL.




    The second  performance measure  shown in Table  5-1  is  the   difference  of




medians between ranked  pairings  of  the  "N"  highest  observed   and  predicted




concentrations.   It should be noted that the 95 percent  confidence  interval  is




determined   with  the  nonparametric  Mann-Whitney   test   for  the   median




difference.  Results  for  the difference of medians are  very similar to those




for the difference of averages.




    The   last   performance  measure   shown  in  Table   5-1   is   the   variance




comparison.  This  statistic was  obtained by dividing the  variance of  the "N"




highest  observed  values  by  the  variance  of   the   "N"  highest  predicted




concentrations.    A quotient  of  1  indicates  equal   scatter  of observed and




predicted  concentrations.   The F  test  was  used  to calculate  the 95  percent




confidence  levels.   Results of the  variance comparison  for  the at-grade data




sets vary.  Of  the at-grade data  sets, the most  notable observation  is that




the models tend to predict  with  much more variability  than  the observed data




(values less than  1).  The GMLINE model  performed relatively better than the






                                      -41-

-------
                 1-HOUR AVERAGE
8-HOUR AVERAGE




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                              -42-

-------
other models  while  PAL performed  poorest;  however  the  95 percent  confidence




intervals on  this  measure  are  fairly  large.   For  the  cut-section data  set




(CAL74), only CALINE3  predicted  with  a variance comparable  to the  observed




variance, while GMLINE  and  HIWAY-2  predicted with much  less variability  than




observed.









    Eight-Hour




    Summary  statistics  for  the  "N"  highest  observed  and  predicted  8-hour




average  CO  concentrations are  presented in Table 5-2  and Figure 5-1  for the




two  largest  data  bases  (NYS77  and  CAL74).    In general,  the  8-hour  model




results  show  the same  tendencies  as the 1-hour results.   For the at-grade data




set   (NYS77),   only  the   GMLINE   model  produces   results   which  are   not




significantly  different  from  observed  concentrations  for  any  performance




measure  listed.  The other  models  all overpredict by a  significant  margin and




produce much more scatter than observed.




    For  the  cut-section  data  base  (CAL74),   all of  the models  underpredict




significantly at the  95 percent level of confidence, as indicated by both the




difference  of averages  and  difference  of medians.   Only  HIWAY2   produces  a




variance  similar  to  the observed  variance.   The GMLINE model  displays  less




scatter  than  observed while  both  CALINE3 and  PAL produce  more scatter  than




observed.









5.2 Statistics for Highest Concentration by Event




    A second  statistical  grouping  compares the highest  observed and predicted




concentrations over the  monitoring  network for each sampling period, paired in




time  (i.e., one pair  of values for each sampling  period).   Statistics  were




generated for both 1-hour and 8-hour sampling periods.
                                      -43-

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    One-Hour




    A summary  of  high by  event results  for the  1-hour averaging  period  is




given  in Table  5-3.   This  table  is  divided  into  two parts  which  present




results  for   paired  (part   one)   and   unpaired   (part   two)   performance




comparisons.   The  number  of  events,  average  observed  value,  and  average




difference are listed  in the  first  three columns  of the paired  part of  the




table.  The average  differences  are  also  shown graphically in Figure  5-2.   In




general, all  models perform  better  using the  SF6  tracer  data than  they  do




using the CO  data,  indicating problems  either with quantifying  CO  emissions or




background  concentrations.    There  is  no  clear   tendency  for  one  model  to




perform   better   than   another,   although  the   PAL   model  overpredicted




significantly  for the  at-grade  CO  data sets.  All four models significantly




underpredicted for the cut-section data  set.




    A second measure of bias, the fraction of events with the highest  observed




concentration  greater  than the  highest predicted  concentration,  is  presented




in column four of  Table  5-3.  The  residual  is defined  as  an observed  minus




predicted   value.    Both   GMLINE   and   CALINE3    underpredict   the   highest




concentrations per  event  at  least  half of  the time  for all  of the  at-grade




data  bases,  while both  PAL and HIWAY2  overpredict the  highest  concentrations




per event at  least  half of  the time for  all the  at-grade  data bases  except




GM75.   Each of  the four  models underpredict  the highest concentrations  for



virtually all events of the cut-section  (CAL74)  data base.




    The  next  three  columns  in  Table   5-3  present   performance   measures




estimating scatter,  and are titled characteristic  discrepancies.   They include




the standard  deviation of  residuals  (noise) with 95  percent confidence limits




calculated from a Chi-Square test, the root mean square error, and  the average




absolute  difference.  Results  for the at-grade  data  bases  show  that  GMLINE




nearly  always  has  the  smallest  values  (best   performance)   in  all  three
                                      -45-

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-------
           GM75
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 NYS76
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 N  =  21
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 N = 45
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        Average Observed
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        CALINE3
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       Figure 5-2.   Average difference of highest observed and predicted one
                    hour concentrations (event by event).
                                    -48-

-------
categories.  In  general,  the  PAL  model  performs worst for this  measure.   For

the cut-section data base,  PAL produces  the largest  standard  deviations,  with

little difference between the models for  the other measures of scatter.

    The  last  column  of  the  first  part  of  Table  5-3  lists  the  Pearson

correlation  of  observed and  predicted concentration pairs for  the  highest by

event  data  sets.   Results  show  that GMLINE  generally  produces the  highest

correlation  coefficients for the  at-grade data sets, ranging  from .53  to  .91;

while  PAL produces  the lowest  correlations  from  .16  to .77.   HIWAY-2  and

GMLINE produce  similar  correlations for  the tracer  data  sets.   All  the models

exhibit poor correlation (.25 or less) for the cut-section data base.

    The  second page  of Table 5-3  (Part 2)  summarizes  unpaired performance

measures  for the  highest  per  event  one-hour  concentration values.   The first

two  columns  of Part 2  repeat the  number of events  and  average  observed value

for  each  data  set.   The ratio of  observed  to predicted variances,  along  with

95 percent confidence  bounds determined by an  F  test,  are presented in column

three.  Variance ratios close  to 1.0  indicate  good model performance.  Results

show  the  GMLINE model  performs best  using  this performance statistic for each

of the  at-grade data sets.   For  the  cut-section data  set  (CAL74), predicted

variances  are  small relative to  observed,  except for  PAL which continues to

predict with a  larger variance than observed.

    A comparison  of observed and predicted frequency distributions  is provided

in the last  column of Table  5-3.  The value listed for each data set and model

represents   the  largest  fractional  difference   between the   observed  and

predicted distribution  functions   when   compared at  the  same   concentration

value.    For  this   measure,  positive  fractional  difference   implies  model

overprediction.   The  value  given  in parentheses  is  the  maximum  absolute

difference   which   is   significantly   different  from  zero,  at   a  95   percent

confidence level,  as  given by the  Kolmogorov-Smirnov  (K-S)  test.  Results for

the  two  at-grade  tracer data  sets  show  all models underpredicting  the  highest
                                       -49-

-------
concentrations  by   event,   with  none  of  the  distributions   significantly




different from observed for the NYS76 data  set.   For the two at-grade CO  data




sets,  the  models generally overpredict, with  no distributions  significantly




different from  observed for the  SRI75 data set.   The frequency  distribution




comparison for  the  cut-section data  base shows  virtually  no overlap  between




distributions of the high by event data sets for any of the  models.









    Eight-Hour




    A summary of high  by event results for  the  eight-hour  averaging period is




given in the top half of Table  5-4.   The comparison of  "all  concentrations"  is




discussed in the next  section.   This table  is also  divided  into  two  parts




which present results  for paired  (part  1)   and  unpaired  (part 2)  performance




comparisons.  Statistics were only generated for the two largest  data bases.




    Paired  results   for  the  at-grade  data  set  (NYS77)   show  that  GMLINE




outperforms   the other  models  for each of  the measures.   GMLINE is  the  only




model  which  does  not  predict  highest  concentrations  by  event  that  are




significantly different  from observed.   It  also produces the lowest values for




all three measures of characteristic discrepancies and  the  highest correlation




coefficient.  All models perform poorly for the cut-section data base (CAL74),




substantially  underpredicting   concentrations  and  producing  extremely  small




correlation coefficients.




    Similar  results  were obtained for the unpaired  analysis  of  high  by  event




eight-hour concentrations  (Part  2 of Table  5-4).  GMLINE performs best for the




at-grade data base,  producing  the most favorable values  for  both the variance




comparison  and  the  frequency distribution comparison.  For  these measures all




models perform poorly with the cut-section data base.
                                      -50-

-------






















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5.3 Statistics for All  Concentrations  Paired in Time  and Space




    One-Hour




    The  largest  data   sets   considered  in  this  evaluation   represent   all




concentration values  paired  in  time  and  location.   One-hour  statistics  are




summarized in Table  5-5 for  both paired  (part  one) and  unpaired (part  two)




performance  comparisons.    Statistics  are  presented   in  the  same  columns




described for the high  by  event analysis.   Figure 5-3 presents  graphically the




average difference of all  observed and predicted one-hour concentrations.




    Results for the one-hour  analysis  of all paired at-grade data  differed for




the  SF6  and  CO data  sets.    HIWAY2  outperformed the  other models  using  the




tracer data  bases,  producing the  lowest  values  of bias and  characteristic




discrepancies  and  the highest   correlation  coefficients.   GMLINE  performed




better  overall   for  the  two  CO  at-grade sets,  although  CALINE3  performed




comparably  with  the  SRI75   data.   Once  again,   all   models  underpredicted




significantly  and with  little  correlation  for  the  cut-section  data  base




(CAL74).




    The unpaired comparisons  are shown in Part  2 of Table  5-5  for  the one-hour




analysis of  all data paired  in  time  and space.  For the  variance comparison,




GMLINE predicted with  essentially the same amount of scatter  as  was observed




for  three  of  the at-grade  data  sets, while  PAL  consistently  predicted  with




less  scatter  than  was  observed.   The  observed  and  predicted  cumulative




distribution  functions are   significantly  different  (at  the  95%  confidence




level) for  all  models and  data  sets  except  PAL with  GM75 and  CALINE3  with




NYS76.




    Results for the cut-section data  base were generally  poor  for all models.




PAL  did  produce  the  lowest  variance comparison, but its predicted variance was




significantly less than observed.
                                      -53-

-------




















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Figure 5-3.   Average difference of all  observed and predicted one hour
             concentrations (paired in time and space).
                               -56-

-------
    Eight-Hour




    Summary statistics  for  the "all  paired"  analysis  of  eight-hour data  are




given in the bottom half of  Table  5-4 (part  1).   Results for the  at-grade  data




set  (NYS77)  show  that  GMLINE  produced the  lowest bias  and  scatter and  the




highest  correlation  of  observed  to  predicted values.   CALINE3  and  HIWAY-2




perform comparably, while PAL  produced  the  largest bias and scatter and lowest




correlation.  All models perform equally for the cut-section data  set (CAL74),




except the correlation by PAL is somewhat lower.




    As  shown  in Part 2  of  Table  5-4,  the variance  comparison  for the  "all




concentration"  data  sets indicates  opposite  performance between  the at-grade




and  cut-section  data  bases.    The  ratio  of  variances  were  significantly




different  from  unity at  a  95% confidence  level  for all models  and both  data




sets  (except GMLINE for  NYS77).   Also  for NYS77,   all  models  except   PAL




produced  frequency  distributions  which  were  similar to the observed at a 95%




level of confidence.   The cut-section data base resulted  in significantly  less




predicted scatter than observed for all  models, but to a lesser degree for  PAL.









5.4 Statistics for Highest Concentration at Each Station




    One-Hour




    Performance  statistics  are presented  in  Table  5-6 for the  comparison of




highest  and  second  highest  one—hour  concentrations  observed and  predicted at




each  station over  the  duration  of each of the  five monitoring programs.




Because  of  the  small number of events   (i.e.,  number  of  monitoring stations)




for most  of  the data bases, caution  should be exercised when interpreting the




statistics.




    The  statistics  provided  in  this  table  compare  observed  and predicted




one-hour values for the number  of  data  pairs  shown in  the  first  column.   This




number  ranges  from  3  for the  NYS77-CO  study to 22  for  the SRI75-CO program.






                                      -57-

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-------
The next  two  columns present  the average  of  the observed  concentrations and




the average difference  between observed  and predicted  values.   Results  show




that GMLINE produces the smallest  bias of  highest  concentrations by  station




for each  at-grade  data  set.   The model  also produces  the  smallest bias  of




second highest concentractions by station for three of  the  four at-grade data




bases.  PAL overpredicts by  an extreme margin  for the  NYS77 data base.   For




the  cut-section  data   set   3  of  the  4  models   underpredict,   while  PAL




overpredicts.




    GMLINE  performs with  the  lowest  noise  (or standard  deviations  of the




residuals)  for  the  at-grade  high  by station   concentrations.   GMLINE  also




produces the  lowest  noise  for three of the four at-grade data sets when second




highest concentrations  are  compared.   The  largest values  of noise  are  found




with the PAL model for NYS77 and CAL74.




    As  can be  seen from  the  results presented  here,   the high  by  station




statistics  are  very  unstable, with large  differences  between  the high and




second high data sets.









    Eight-Hour




    Results of  the  eight-hour high and  second  high  by station  analyses are




presented  in  Table  5-7.  All  of  the models  overpredicted for  NYS77,  with




GMLINE  showing  the  least  bias and  PAL the  greatest.  Underprediction  was the




rule  with  CAL74 data,  except  for the  PAL results for  high by  station which




overpredicted.  The lowest values  of noise are  found  for  GMLINE with NYS77.




This  statistic  is  unstable,  however,  as  evidenced  by PAL for CAL74,  which




exhibits the  largest noise  for the  high  concentrations  and the smallest noise




for the second  high concentrations.   Again, caution  should  be  exercised when




analyzing  the confidence intervals  for these small  data sets,  since  many of




the assumptions are not met.






                                      -59-

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-60-

-------
5.5 Examples of Statistics by Subsets




    While the  model  performance statistics  presented earlier are  informative




for comparing  model  performance for applications in  regulatory  settings,  the




results  do  not  provide much  information  as  to why individual  models  are




performing good or badly.   A better understanding of  model  performance  can be




obtained by analyzing  results for  different subsets  of  the basic data  base.




In  this  section examples  of the subset  results are presented for two  of  the




data bases,  GM75 and NYS77.  The General Motors data base was  selected because




it  probably contains  the  best  quality  data.   Since  it was  a tracer  (SF6)




study, there is no contamination of ambient air quality data  by  background or




other  nearby  sources.   As  shown  in the previous results sections, the  models




perform  best  for the  GM  data  base.   The  1977  New York  State  (Long  Island




Expressway)   CO  data  base  was  selected  because  it  is  typical  of  highway




modeling  applications  and  it produced distinct differences  between  overall




model  performances.    The  NYS77  data  base  also contains  a  large number  of




events (over 300) for statistical analysis.




    Results  by subset  are  presented for  "N" highest  and  paired in  time  and




space data sets only.








    "N" Highest Observed and Predicted Values




    In Table  5-8  the difference of  averages  are  presented  for the "N" highest




observed  and  predicted SF6  concentrations for the  GM75 data  base.   In  this




table  the first  column of  data  contains the  average observed value for  all




stations combined, by station and by subsets of meteorological conditions.




    The  difference of  averages  for GMLINE  shows little bias  for any  of  the




subsets.  However,  the largest  underprediction  (positive  value) occurs  near




the roadway  at Station 3  (see Section  3  figures),  and for higher wind speeds,




unstable  conditions  and near  parallel  wind/road angles.   It should  be  noted
                                      -61-

-------
                                   TABLE 5-8

              COMPARISON OF DIFFERENCE OF AVERAGES .FOR "N" HIGHEST
                OBSERVED AND PREDICTED SF6 CONCENTRATION  VALUES
               (UNPAIRED IN TIME OR LOCATION)  FOR VARIOUS SUBSETS
                 DATA BASE:   GM75    AVERAGING TIME:   HALF HOUR
   Data Sets
("N" of Events)
   Lesser of
   25 or 25%
 Average
 Observed
Value (Co)
  (PPB)
 Difference of Averages (C0-CP)

            Model
GMLINE   CALINE3
         PAL   HIWAY2
1.  All Stations/All
     Events(16)
   3.4
   .1
-1.9    -1.9    -.1
2. By Station/All
Events
Station 1(16)
Station 2(16)
Station 3(16)
Station 4(16)
Station 5(16)
Station 6(16)
Station 7(16)
3. By Meteorology
A. Wind Speed
< 2.0 sec (7)
2.0 to 4.0 (9)
> 4.0 sec (0)
B. Stability Group
Class UNST (7)
Class NEUT (2)
Class STAB (7)
C. Wind/Road Angle
<20. Degrees (6)
20. to 60. (6)
>60. Degrees (5)


.9
2.8
3.1
1.9
1.2
.9
.5


3.4
3.3
—

2.9
2.9
3.7

3.7
3.3
2.9


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.1
.4
.2
.1
.0
.0


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.2
—

.2
-.2
.1

.2
.0
.1


-2.2
-.7
-.8
-1.0
-1.2
-1.3
-1.4


-3.3
-.5
—

.4
-9.2
-.9

-3.9
-.3
-.4


-.6
-.7
-1.7
-2.0
-1.8
-1.5
-1.0


-1.0
-2.6
—

-.1
-1.6
-3.0

-3.5
-.3
.6


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-.1
.1
-.4
-.5
-.4
-.3


-.4
.3
—

.4
-.5
.0

.1
-.1
.0
                                      -62-

-------
that the GM75 data base contains  no averaging periods with wind  speed greater




than  4.0 m/s  and only  eight  periods  of  neutral stability  (i.e.,  N =  2).




Therefore the statistics for  some of the subsets  are  fairly  unstable  and  the




results should be viewed with this in mind.




    The CALINE3 and  PAL  models in general overpredict  the "N" highest values




by a  much greater extent than GMLINE underpredicts (or  HIWAY-2  overpredicts).




These  overpredictions by  CALINE3  and  PAL  seem  to  occur  under  different




circumstances  for the  two  models.  The  largest  overpredictions by CALINE3




occur  for  light wind speeds,  neutral  stabilities and  low wind/road angles,




while  PAL overpredicts more  for higher wind  speeds,  stable  conditions and low




wind/road angles.




    HIWAY-2  shows a  small  amount  of  negative bias  (overprediction)  overall,




with  little distinction between the subsets by station or meteorology.




    Table 5-9  shows  similar  results  for  the NYS77 data base.  This  data base




shows  more  distinct  differences  between   the   models   than  GM75.   GMLINE




overpredicts  to  a  lesser  degree  than the  other three  models.  Only  minor




difference  exist between  stations for all  the  models.   The largest GMLINE




overpredictions  are  for  light  wind  speeds, neutral  stabilities   and  near




parallel  wind/road  angles,  while  underpredictions  occur  for  higher  wind




speeds.   In fact, this  same  tendency  is  found for all four  models,  with the




largest overpredictions by PAL.








    Observed and Predicted Values Paired in Space and Time




    Table 5-10  presents model  performance  statistics  (difference of averages)




for subsets of  events paired in  space  and  time for  the  GM75 data  base.   The




same  subsets are  shown  in the previous  section  for  "N"  highest values.  The




number of events  is much  larger  for  each of  the paired subsets,  while  the




average  observed  and predicted values are somewhat lower  since the full set of
                                      -63-

-------
                                   TABLE 5-9

             COMPARISON OF DIFFERENCE OF AVERAGES FOR  "N" HIGHEST
                OBSERVED AND  PREDICTED CO CONCENTRATION  VALUES
               (UNPAIRED IN TIME  OR LOCATION)  FOR VARIOUS SUBSETS
                  DATA  BASE:   NYS77-CO AVERAGING TIME:  1 HOUR
   Data Sets
("N" of Events)
   Lesser of
   25 or 25%
 Average
 Observed
Value (Co)
  (ppm)
  Difference of Averages (C0-CP)

	Model	
GMLINE   CALINE3    PAL   HIWAY2
1.  All Stations/All
     Events(25)
   4.7
-2.4     -12.5    -37.3   -8.7
2. By Station/All
Events
Station 1(25)
Station 2(25)
Station 3(25)
3. .By Meteorology
A. Wind Speed
< 2.0 sec (25)
2.0 to 4.0 ,(25)
> 4.0 sec (14)
B. Stability Group
Class UNST (11)
Class NEUT (25)
Class STAB (14)
C. Wind/Road Angle
<20. Degrees (15)
20. to 60. (25)
>60. Degrees (25)


2.2
4.2
3.9


3.9
3.8
3.8

3.6
4.6
2.0

4.0
3.8
3.8


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-1.6
-1.3


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1.3

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-2.5
-1.0

-2.8
-.4
-.5


-4.5
-8.0
-8.7


-13.4
-.3
1.4

-2.9
-11.2
-5.7

-14.5
-4.5
-1.5


-19.2
-25.1
-22.7


-38.1
-1.9
.6

-6.4
-31.0
-17.3

-38.4
-14.6
-5.9


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-5.8
-5.7


-9.5
-.7
1.1

-1.5
-8.4
-3.4

-6.4
-2.3
-5.6
                                      -64-

-------











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-------
values  are  included  in  this   analysis.    For  the   GMLINE  model   a  slight




underprediction  occurs  for  the  all-paired  data  set,  but  the  analysis  by




station  shows  that  underprediction  at  Stations  2  through  4  are  primarily




responsible for  this.   Very little  difference  exists between  meteorological




subsets  for  the  GMLINE  model.   CALINE3  and  PAL   show  nearly  equivalent




overprediction as  indicated  by  the  all-paired  in time  and  space  data  set.




CALINE3  shows  more  pronounced  overprediction  for light  wind  speed,  neutral




stability   and  near   parallel   wind/road  angles,   while   PAL's   largest




overpredictions  occur  with  both neutral  and stable  conditions and  also  for




near parallel  wind/road angles.   The  HIWAY-2 model shows  no  significant  bias




for any of the GM75 data sets shown.




    Similar  results  for  the NYS77  data  base  are displayed in  Table  5-11.




There were  only  three monitoring  stations for  this data base,  but  the number




of  events  is  somewhat  larger  than  for  GM75.   All  four  models  tend  to




overpredict for  each station and meteorological subset,  except for the highest




wind speed (greater than 4 m/s).   All four models  underpredict  for  the higher




wind  speeds.   GMLINE  also  shows  some  slight  underprediction for  unstable




conditions.




    Again,  the  tendency  by  all  four  models  is  to  produce  the  largest




overpredictions  for  light  wind  speeds  and  near  parallel  wind/road  angles.



Differences between  the models,  though,  show GMLINE  overpredicting the least




and PAL overpredicting the most.
                                      -66-

-------














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-------
6.0 SUMMARY AND CONCLUSIONS

    A  systematic  evaluation  of  model  performance has  been  carried out  for

mobile  source  air  quality  models.    The   evaluation  approach   involves

comparisons  of observed  concentrations  and  model  predictions.   Statistical

measures recommended by the  American Meteorological  Society have been  used to

assess model  performance.   As with  the previous evaluation studies  for other

model categories,  the  primary objective of the current  project  is to  produce

performance  statistics so  that  EPA  and  a  group of  reviewers  may  judge  the

relative merits of different models.

    No  diagnostic  analyses  have  been  performed to  better  understand  why

individual models perform  the way they do.  Statistics were generated though,

for  subsets  of  events  by  station,  wind  speed,   stability  category,  and

wind/roadway  angles.   The  volume  of  information provided   in  the  appendix

tables should provide a useful starting point for such diagnostic analysis.



    Summary of Procedures

    Data from  five  field  studies were  prepared for use  in the  evaluation of

four mobile source models.  The data bases and models are listed below:



         Models                Data Bases

         CALINE3               GM75 - General Motors (SFS)
         GMLINE                NYS76 - Long Island Expressway (SF6)
         HIWAY-2               SRI75 - SRI Highway 101 (CO)
         PAL                   NYS77 - Long Island Expressway (CO)
                               CAL74 - Santa Monica Freeway (CO)


    A  summary  of  the data bases,  models,  and  statistics  to be  generated  was

prepared as  part  of a test run package, which also included two-hour test runs

for each model with each data base.   This test  run  package was  submitted to

the  respective model  developers  for review  and comment.  Responses  to  the
                                      -69-

-------
comments were  then  made,  the  models were  run  for the  full  data bases  and

statistics were generated.



    Summary of Results

    The  results  are presented  in  Section 5  and in the  Appendix A  through D

tables.  The most distinct results  are highlighted in the following statements:


    •  Better model  performance was  found for the  SF6  tracer data  bases
       than for the CO data sets.

    •  The models  exhibit  distinct differences  in  performance within any
       individual data set.

    •  Bias  for  the 1-hour  and 8-hour averages  showed similarities  for
       each model.

    •  All of  the  models  underpredicted significantly for the cut-section
       
-------
7.0 REFERENCES

Bemis,  G.R.,  P.E.  Benson,  A.J.  Ranzieri  and  K.O.   Pinkerman,  1977:   Air
    Pollution and  Roadway Location, Design  and Operation  -  Project Overview.
    FHWA-RD-77-102.

Benson, P.E.,  1979:   CALINE3  -  A  Versatile  Dispersion  Model for  Predicting
    Air    Pollutant     Levels    Near    Highways    and   Arterial    Streets.
    FHWA/CA/TL-79/23,  California Department of Transportation, Sacramento, CA.

Bullin, J.A., J.C. Polasek and N.J. Green, 1978:  Analytical and Experimental
    Assessment of  Highway Impact on Air Quality.  FHWA/TX-79/11-218-4, Federal
    Highway Administration, Washington, D.C.

Cadle, S.H.,  D.P.  Chock,  J.M.  Heuss,  and  P.R.  Monson,  1976:  Results  of the
    General  Motors Sulfate  Dispersion  Experiment.   G.M.  Research  Publ.  No.
    2107, General Motors Research Laboratories, Warren, MI.

Chock,  D.P.,  1977a:   A  Simple  Line  Source  Model   for   Dispersion  Near
    Roadways.    GMR-2407,   General  Motors  Research  Laboratories,  Warren, MI.
    (see also Atmospheric Environment,  Vol. 12, pp.  823-829 (1978).)

Chock,  D.P.,  1977b:    General  Motors  Sulfate  Dispersion  Experiment  -  An
    Overview  of  the  Wind,  Temperature and Concentration Fields.  Atmospheric
    Environment,  Vol.  11,  pp 553-559.

Chock,   D.P.,  1977c:    General   Motors   Sulfate   Dispersion   Experiment:
    Assessment of  the  EPA HIWAY Model.  J. Air Poll.  Control Assoc., Vol. 27,
    pp 39-45.

Computer  Sciences  Corporation,  1983:   Modeling  Evaluation  Support  System
    (MESS)  Data  Management Subsystem  (DMSS)  User's  Manual.   Computer Sciences
    Corporation,  Durham, NC.

Dabberdt,  W.F.,  E.  Shelar, D.  Marimont,  and  G.   Skinner,   1981:   Analyses,
    Experimental Studies,  and  Evaluations  of Control Measures for Air Flow and
    Air Quality On and Near Highways, Vol. I:   Experimental Studies, Analyses,
    and Model Development.  FHWA-RD-78-179, SRI International, Menlo Park, CA.

EPA,    1977:      User's    Manual    for    Single-Source    (CRSTER)    Model.
    EPA-450/2-77-013, U.S. EPA, Research Triangle Park, NC.

EPA,   1981:   User's  Guide  to  MOBILE2   (Mobile   Source  Emissions  Model).
    EPA-406/3-81-006, U.S. EPA, Research Triangle Park, NC.

EPA,  1984:   Draft User's Guide to  MOBILES  (Mobile Source  Emissions Model).
    EPA-406/3-84-002, U.S. EPA, Research Triangle Park, NC.

Fox, D.G.,  1981:    Judging Air  Quality  Model Performance  (A Summary of the AMS
    Workshop  on  Dispersion Model  Performance, Woods  Hole,  MA,  8-11 September
    1980).  Bull. Am. Meteorol. Soc., 62, 599-609.

Hirtzel,  C.S. and J.E.   Quon,  1981:  Estimating  Precision  of  Autocorrelated
    Air   Quality   Measurements.    Summary  of  Proceedings   Envirometrics  81,
    200-201.
                                      -71-

-------
Hollander, M. and R.A.  Wolfe,  1973:   Nonparametric Statistical Methods.   John
    Wiley and Sons,  New York,  NY.

Londergan, R.J.,  D.H.,  Minott, D.J.  Wackter, T.M. Kincaid and D.M.  Bonitata,
    1982: Evaluation of Rural  Air Quality Simulation  Models.  EPA-450/4-83-003,
    OAQPS, Research Triange Park,  NC.

Londergan, R.J., D.H. Minott,  D.J.  Wackter and R.R.  Fizz, 1983:   Evaluation of
    Urban Air  Quality  Simulation  Models.   EPA-450/4-83-020,  OAQPS,  Research
    Triangle Park, NC.

Ludwig,   F.L.,   and  W.F.  Dabberdt,   1976:   Comparison  of  Two  Practical
    Atmospheric Stability Classification  Schemes  in  an Urban Application.   J.
    Applied Meteorology, 15, 1172-1176.

Martinez,  J.R.,  H.S.  Javitz,  R.E.  Ruff,  A.  Valdes,   K.C.  Nitz,  and  W.F.
    Dabberdt, 1982:   Development and Application of Methodology for Evaluating
    Highway  Air  Pollution  Dispersion   Models.    Prepared  by  SRI  for  the
    Transportation Research Board  of the  National Research Council,  National
    Cooperative  Highway Research  Program Report  245,  Washington,  D.C.   (SRI
    Project 8363)

Petersen, W.B.,  1978:   User's  Guide  for  PAL, A  Gaussian Plume  Algorithm for
    Point,  Area,  and  Line  Sources.   EPA-600/4-78-013,  U.S.  EPA,  Research
    Triangle Park, NC.

Petersen,  W.B.,  1980:   User's Guide  for  HIWAY-2,   A  Highway  Air  Pollution
    Model.  EPA-600/8-80-018,  U.S.  EPA,  Research Triangle Park,  NC.

Rao, S.T., M.  Chen,  M.  Keenan, G. Sistla, R.  Peddada,  G. Wotzak and N. Kolak,
    1978:  Dispersion of Pollutants  Near Highways.   Experimental Design and
    Data   Acquisition  Procedures.   EPA-600/4-78-037,   U.S.  EPA,   Research
    Triangle Park, NC.

Rao,  S.T.,  M.  Keenan,  G.  Sistla  and  P.  Samson,  1979:    "Dispersion  of
    Pollutants   Near   Highways.    Data  Analysis   and  Model   Evaluation."
    EPA-600/4-79-011.  U.S. EPA, Research Triangle Park, NC.

Snedecor,  G.W.  and  W.G.  Cochran,  1967:   Statistical  Methods,   6th  Edition.
    Iowa  State University Press, Ames, IA.

Wolf,  D.E.,  E.  Shelar,  R.E.  Ruff,  and  W.F.  Dabberdt,  1981:   Analyses,
    Experimental Studies, and Evaluations of Control Measures for Air Flow and
    Air  Quality  On  and Near Highways; Volume III - User's Manual for FHWA Data
    Base   and    Retrieval    Programs.     FHWA/RD-81/054,    Federal   Highway
    Administration, Washington, D.C.

Wackter,  D.J. and  R.J.  Londergan,  1984:   Evaluation  of Complex  Terrain Air
    Quality Models.   EPA-450/4-84-017, OAQPS, Research Triangle Park, NC.

Zimmerman, J.R.  and R.J. Thompson,  1975:  User's Guide  for HIWAY,  a Highway
    Air  Pollution Model.  EPA-650/4-74-008,  U.S.  EPA,  Research Triangle Park,
    NC.
                                      -72-

-------
           APPENDIX A




STATISTICS FOR HIGHEST "N" VALUES

-------
    In  the  high  N  tables,  model performance statistics  are presented  which




compare the highest  "N" (lesser of 25 percent  or the 25 highest) observed  and




predicted  concentration values,  unpaired  in time  or  location.   Each  table




contains the results for one model, one  data  base base and one  averaging time




(1 or  8  hours).   In each table, results are  presented for the entire data set




(all  stations/all  events)  and  for  subsets  representing  either  a  single




monitoring  station  or particular meteorological conditions.   Subsets  based on




meteorology were  developed  only for  the 1-hour averaging  time.   In an  effort




to mitigate statistical problems due  to spatial correlation,  selection rules




stipulated that only one value could be selected from a given event.




    The  first  two columns of results in the  Appendix A tables  are simply the




average of the "N" highest  observed values  and the  average of  the  "N" highest




predicted  values  for  each data  set.    The  first  performance measure  (third




column)  is  the difference  between  these averages.   A positive value implies




model  underprediction.   In parentheses  following  the calculated value  is the




95 percent confidence interval, determined by using the  two-sample  Student's t




test.




    The  second performance measure  is  the difference  between the  medians of




the  observed  and  predicted values.   The 95  percent confidence interval  (in




parentheses) is determined using the nonparametric Mann-Whitney test.




    The  third  performance  measure is the variance comparison.  The variance of




the  highest observed  values was  divided  by the  variance of  the  predicted




values.   The  variance  ratio  is  presented,  together with the  95  percent




confidence  interval (in parentheses), determined using an F test.




    The  final  performance  measure in  Table A  is  the  frequency  distribution




comparison.  The cumulative distribution function f(C)  represents  the fraction




of  the data set  (in this  case,  the  fraction of the  "N"  highest  data points)




with concentrations values  less than or  equal  to C.   The value presented in
                                       A-l

-------
this  column  is  the  largest  difference between  the observed  and  predicted




distribution functions (for the  same concentration values)  obtained when  the




two functions are compared for all concentration values.




    A  positive  fractional  difference  implies  model  overprediction.   At  the




concentration  value where  the  maximum  difference  occurs,  the  fraction  of




observed values less than or  equal  to  that  concentration value is  larger than




the fraction  of  predicted values.   Conversely,  the fraction of observed values




higher than that concentration value is smaller  than the fraction of predicted




values.




    The  value  given  in  parentheses   is  the   maximum  difference  which  is




significantly different from  zero,  at  a 95 percent confidence level, as given




by the Kolmogorov-Smirnov (K-S)  test.   This confidence interval is  a function




of the number of cases.
                                       A-2

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TABLE A19
COMPARISON OF "N" HIGHEST OBSERVED AND PREDICTED
CO CONCENTRATION VALUES (UNPAIRED IN TIME OR LOCATION)
SES STATISTICS FOR MODEL/DATA BASE: PAL/CAL74 AVERAGING TIME: 1 HOUR
AVERAGE AVERAGE DIFFEREHCE DIFFERENCE VARIANCE FREQUENCY
DATA SETS OBSERVED PREDICTED OF AVERAGES* OF MrDIANS* COMPARISON* DISTRIBUTION
("N" OF EVENTS) VALUE (CO) VALUE (CP) (CO - CP) (CO - CP) ( SO**2/SP**2 ) COMPARISON*
LESSER OF (PPM) (PPM) (PPM) (PPM) (F03S-FPRED
25 OR 25X (FRACTION)
1. ALL STATIONS/ALL
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                  APPENDIX B

STATISTICS FOR VALUES PAIRED IN TIME, INCLUDING
  HIGH BY EVENT AND  PAIRED IN SPACE  AND TIME

-------
    In the Appendix B  tables,  model performance  statistics  are presented  for




all  events  with  either the predicted  or the  observed concentration above  a




threshold value.   As in  the  previous  section, each table contains  the results




for one model, one  data base,  and one averaging time.   In each two page table,




results are first presented  based on comparisons of the  highest observed  and




predicted  concentration  values   event-by-event,   regardless  of   location.




Results are  then presented  for  observed and  predicted  concentration  values




paired in time and  location, including results for all stations combined,  for




each  individual   monitoring  station,  and  for  subsets  of  events  based  on




meteorological conditions.   As noted previously,  subsets by  meteorology were




employed only for the 1-hour averaging time.




    In establishing  the sets  of  observed and  predicted  concentration data,  a




small  threshold  concentration  was used  to  eliminate events  when  both  the




observed and  predicted values  at  a monitoring station were insignificant.  The




threshold value  for  1-hour average  carbon  monoxide   concentrations  was  0.5




ppm.   No threshold was used  for  8-hour  average  CO concentrations or  SF6




concentrations.




    Each table  of results is  presented  in two parts,   due to  the  large number




of  performance measures  recommended for these  data   sets.   Part 1  provides




results  based on  statistics  appropriate  to  unpaired  data.  Part  2  gives




results  obtained for  the same  data  sets  using statistics  appropriate  for



paired data.




    In Part 1, the first  column presents the number of events (or data pairs)




in  each data set.   The  second and  third columns present,  respectively,  the




average  observed and  average predicted concentration  values.   The  fourth




column  presents  the  difference  between  the  observed  and  predicted  averages,




followed in parentheses  by  a 95 percent  confidence interval  calculated with a




two-sample t  test.   The fifth column (variance comparison)  presents  the ratio
                                       B-l

-------
of the observed  variance divided by  the predicted  variance,  with 95  percent




confidence bounds in parentheses as  calculated with an F test.




    The frequency distribution  comparison  (sixth column) is equivalent to  the




performance  measure  previously  described  in  Appendix  A.    The  cumulative




frequency distribution of observed  values  is compared  to the  distribution of




predicted  values.   The  maximum fractional  difference  is  listed; a  positive




difference  implies  model overprediction.   The  value  in parentheses  is  the




maximum difference  significant  at  a  95 percent confidence level,  as  given by




the K-S test.




    In Part 2,  the number  of  events  (i.e.,  data pairs)  is  repeated in  the




first column.  The average difference between observed  and  predicted  values is




presented  in the  second column.   In  parentheses,  the 95 percent confidence




interval calculated with a one-sample t test is given.




    The fraction of positive residuals is presented in the third column.   This




performance  measure indicates  the  fraction of  observed-predicted data  pairs




for  which  the  observed concentration value  is  larger  than the  predicted




value.   In  the  fourth  column, the  standard deviation  of the  residuals  is




presented,  with  95 percent confidence  bounds  (from a  Chi-Square   test)  in




parentheses.  The  root  mean  square  error  of the  residuals (i.e., the square




root  of  the average squared residual) is  given next,  followed by the average




absolute residual.




    The   Pearson   correlation  coefficient   of   observed   and   predicted




concentration values is  presented in  the  seventh  column.  In  parentheses, 95




percent  confidence  bounds   on the  correlation  coefficient   are  listed,  as




calculated  with  the Fisher z test.   The Part 2 tables  are  also continued on a




second page for  presentation of meteorological subsets.
                                       B-2

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TABLE B25. (PART 1) COMPARISON OF OBSERVED AND PREDICTED
CO CONCENTRATION VALUES EVENT-BY-EVENT (PAIRED IN TIME)
SES STATISTICS FOR MODEL/DATA BASE-' GMLH/CAL74 AVERAGING TIME: 8 HOUR
PERFORMANCE MEASURES - UNPAIRED COMPAPISOMS
AVERAGE AVERAGE DIFFERENCE VARIANCE FREQUENCY DISTRIBUTION
DATA SETS NUMBER OBSERVED PREDICTED OF AVERAGES* COMPARISON* COMPARISON*
OF VALUE (CO) VALUE (CP) (CO - CP) (F(CO) - F(CP)) MAX
EVENTS (PPM) (PPM) (PPM) (SO**2/SP**2 ) (FRACTION)
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                  APPENDIX C




STATISTICS FOR HIGHEST CONCENTRATION BY STATION

-------
    In the high  by  station tables, model performance  statistics  are presented




which compare the maximum  observed and predicted concentrations  over  all time




periods  at  each monitoring  station.   Each  table  contains  results  for  one




model, one data  base,  and  one  averaging time.   The performance  measures  are




analogous to those in Part 2 of the Appendix B tables.




    Statistical  analyses  in  these tables  compare  the  N  pairs   of  observed-




predicted  values,  where N  represents the number  of stations.   The  first  two




columns  present   the   averages   of   the  maximum  observed  and   predicted




concentration values  at each  station.   The third column  presents the average




difference  between  observed  and  predicted  values,   with   the  95   percent




confidence interval in  parentheses (calculated with a  one-sample  t test).  The




fourth column  indicates the  fraction of observed-  predicted  data pairs  for




which  the  observed  value  is  greater  than  the  predicted.   The  standard




deviation  of  the residuals  (differences)  is  presented  in  the  fifth column,




with  95  percent  confidence  bounds as  given by a  Chi-Square test.   The root




mean  square  error  (square  root of  the  average  squared  residual)  is  listed




next,  followed  by the  average absolute residual.   The  Pearson correlation




coefficient  indicates  the  spatial correlation  of  the  maximum  concentration




values at  each  station.   The 95 percent  confidence  bounds for the correlation




coefficient were calculated using a Fisher z test.
                                       C-l

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                    APPENDIX D




STATISTICS FOR HIGHEST VALUES FOR VARIOUS PAIRINGS

-------
    The  high  value  tables present  single-value  residuals  representing  the


difference between  the  highest observed  (or predicted) value over  all  events


and locations  and the corresponding  predicted (observed)  value  for the  same


event or  location.  Each  table  contains results for one  model,  one data base,


and one  averaging time.    The  tables  contain  no  statistical  measures,  since


confidence intervals cannot be assigned for single-value comparisons.


    The  first  row  presents   the  highest  observed   and   highest   predicted


concentration values, over all events  and locations.  The  difference  between
 »

observed  and  predicted values  is  given  in  the  third  column.   A  positive


difference indicates model underprediction.


    The  second  row  presents  the maximum observed  concentration,  over  all


events and  locations,  and  the  corresponding predicted value at  the same  time


and  location.   Row  three presents  the  same  observed  value,   but  with  the


maximum  value  at any location during  the same time  period as  the observed.


Row four  again  presents the same  maximum  observed value,  but  matches it  with


the maximum predicted value for any event at the same location as the observed.


    The  last three  rows  repeat the analogous single-value  residuals obtained


using  maximum  predicted  values   instead  of  observed.   Again,  results  are


presented representing pairings in space  and time,  pairings  in time but  not


space, and pairings in space but not  time.
                                       D-l

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                                   TECHNICAL REPORT DATA
                            (Please read Instructions on the reverse before completing)
1. REPORT NO.
  EPA-450/4-86-002
                              2.
                                                            3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
   Evaluation of Mobile Source Air Quality Simulation
   Models
                                                            5. REPORT DATE
                                                             February  1986
             6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
                                                            8. PERFORMING ORGANIZATION REPORT NO.
   David Wackter and Paul  Bodner
9. PERFORMING ORGANIZATION NAME AND ADDRESS

  TRC  Environmental Consultants, Inc.
  800  Connecticut Boulevard
  East Hartford, Connecticut   06108
                                                            10. PROGRAM ELEMENT NO.
              11. CONTRACT/GRANT NO.
                68-02-3886
12. SPONSORING AGENCY NAME AND ADDRESS

  U.S.  EPA
  OAQPS,  MDAD, SRAB (MD-14)
  Research Triangle Park, NC  27711
                                                            13. TYPE OF REPORT AND PERIOD COVERED
              14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT


      This  report  summarizes the  result of a comprehensive evaluation of  four mobile
 source air quality simulation models using data collected from five field  measurement
 programs.   The report contains numerous tabulations  of  each model's performance in
 terms of statistical  measures of performance recommended by the American Meteorologica
 Society.

      The purpose  of the report is  two-fold.  First,  it  serves to document  for the
 models considered, and similar models, their relative performance.  Second,  it pro-
 vides the  basis for a peer scientific review of the  models.  To stay within  the spirit
 of this latter purpose, the report is limited to a factual  presentation  of information
 and performance statistics.  No  attempt is made to interpret the statistics  or to
 provide direction to the reader,  lest reviewers might be biased.
17.
                                KEY WORDS AND DOCUMENT ANALYSIS
                  DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS  C.  COS AT I Field/Group
 Mobile  Source Air Quality Models
 Carbon  Monoxide
 Statistical  Measures
 Performance  Evaluation
 Mobile  Source Data Sets
18. DISTRIBUTION STATEMENT
                                               19. SECURITY CLASS (This Report/
                           21. NO. OF PAGES
                             236
                                              20. SECURITY CLASS (TMs page)
                                                                          22. PRICE
EPA Form 2220-1 (Rev. 4-77)   PREVIOUS EDITION is OBSOLETE

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DAT

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