EPA-AA-TSS-I/M-87-l

                  Technical  Report
          A Discussion  of Possible Causes  of
   Low Failure Rates in Decentralized I/M Programs
                          By

                  Eugene J. Tierney


                     January 1987
                        NOTICE

Technical  Reports  do  not necessarily  represent  final  EPA
decisions  or  positions.   They  are  intended  to  present
technical analysis of  issues  using data which are currently
available.   The  purpose in the  release of such  reports is
to facilitate  the  exchange of technical  information and to
inform the  public  of technical developments  which may  form
the basis  for  a final  EPA decision,  position or  regulatory
action.

                Technical Support  Staff
         Emission Control Technology Division
               Office of Mobile Sources
              Office  of  Air and  Radiation
         U.  S.  Environmental Protection  Agency

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ABSTRACT


     This  technical  report  reviews  six possible  explanations
for  low reported  failure  rates  in  manual,  decentralized  I/M
programs.   The report  analyzes  and  discusses  random  roadside
idle survey data,  reported  I/M program data and  data collected
during  audits  of  I/M programs.  The  data indicate  that  five of
the   explanations:    quality   control,   fleet   maintenance,
differences  in fleet  mix or  emission  standards,  anticipatory
maintenance,  and  pre-inspection   repair,  do  not  sufficiently
explain  low  reported failure  rates.   The report concludes that
the major problem contributing to  low reported  failure  rates in
decentralized,  manual  I/M programs   is  improper  inspections by
test station personnel.

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                  TABLE OF CONTENTS

    SECTION                                     PAGE

Abstract                                          i
Table of Contents                                 ii
List of Tables                                    iii
List of Figures                                   iv


Introduction                                      1
Quality control issues                            4
Better maintained fleet                           6
Standards and coverage differences                6
Anticipatory maintenance                          7
Pre-inspection repair                             9
Improper Inspection                               9
Conclusions                                       25
References                                        26
                          11

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                         LIST OF TABLES
TABLE             TITLE                                  PAGE
  1      Failure Rates in I/M Programs                     2

  2      Potential Causes For Lower Than
               Expected Failure Rates                      3

  3      Potential Impact of Quality Control
               Deficiencies on I/M Failure Rates           5

  4      Non-I/M Vehicle Failure Rates Using
               Consistent Emission Standards               6

  5      Reported Failure Rates vs. Survey Failure Rates   10
                               111

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                        LIST OF FIGURES


FIGURE       TITLE                                       PAGE


  1      Comparison of  Idle  Surveys                         11

  2      Frequency Distribution of Emission  Scores          13

  3      Cumulative Distribution of  Emission Scores         14

  4      Initial  Test Scores Cumulative Distribution
               Model Year  1977                              15

  5      Retest Scores  for Connecticut versus Initial      16
               Test Scores,   Model Year 1977

  6      Initial  Test Scores Cumulative Distribution
               Model Year  1980                              17

  7      Retest Scores  for Connecticut versus Initial      18
               Test Scores,   Model Year 1980

  8      Initial  Test Scores Cumulative Distribution
               Model Year  1982                              19

  9      Retest Scores  for Connecticut versus Initial      20
               Test Scores,   Model Year 1982

 10      Repeat Index - Colorado                           22

 11      Repeat Index - Virginia                           23
                               IV

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INTRODUCTION

    Inspection  and maintenance  (I/M)  programs  are  currently
operating  in  thirty-one  states  and affect  approximately  one
third of  all  light  duty  cars and  light  duty trucks  in  the
country.   The  annual  inspection  cost of  these programs  is  in
the  neighborhood  of  $500  million.   Given   the   significant
impact,   it   is  important  to  carefully  assess  the  outcomes  of
these  programs  on an  individual  basis  with  an   eye  toward
effectiveness  and  cost-efficiency.   The  object   of   an  I/M
program is  to  identify vehicles  that are  "gross  emitters"  of
hydrocarbons  (HC)  and/or  carbon  monoxide  (CO)   and  require
emission-related repairs of those  vehicles such that  emissions
are   reduced.    Thus,   one   important   indicator   of   program
effectiveness is the percentage of vehicles that  are identified
as gross emitters (i.e.,  fail  the emissions test).

    Two  basic  approaches  to  inspecting  vehicles  have  been
implemented  among  I/M  programs:  centralized and  decentralized.
In centralized programs, motorists  bring  their vehicles to high
volume  test   facilities   operated  by   the   state  or   local
government  or   by a  contractor  hired  by  the  state  or  local
government.   The repair  function  is  independent  of  the  test
function  and the  centralized  facilities  are  generally  highly
automated and systematic.  Decentralized  I/M  programs  generally
have  few or  no  high  volume  stations.    The  state  or  local
government  licenses  service   stations,  automobile  dealerships
and the  like to do  inspections.   Motorists have the  option of
obtaining repairs  at the  licensed facility or  going elsewhere.
Two distinct  types of decentralized  inspection programs exist:
ones  that  use  manual  emission  analyzers and ones  that  use
computerized  analyzers.    In   the   latter  case,  a   computer  is
built into  the analyzer that  controls  the test  procedure,  the
selection of emission standards,  the pass/fail  decision,  data
recording,   and  quality   control.    In   the   case   of  manual
analyzers,  no  computer  is   available;   so,   the  inspector  is
responsible  for quality  control,  chooses emission  standards,
reads meters or digital  displays for emission levels,  decides
pass/fail status, and records the test data.

    In its role as an oversight agency, EPA has been conducting
audits under  the National Air  Audit System guidelines  and has
been gathering data collected  by individual I/M  programs.   EPA
has  also  been  conducting  random  roadside  tampering  and idle
surveys  in  cities  throughout  the  country   for  many  years.
Analysis  of  these data has revealed some significant findings:
first, emission test failure rates  in I/M programs  vary widely,
from  a  low of  about  2%  to  a high of  28%   (see  Table  One);
second,   failure  rates  vary   by  program  type.   Decentralized
programs  with  manual analyzers  tend to  have  very  low failure
rates  while  centralized   programs   and  decentralized  programs
with  computerized  analyzers tend  to have much higher  failure
rates.

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                           Table One

          EMISSION TEST FAILURE RATES IN I/M PROGRAMS*


                        REPORTED       EXPECTED      RATIO

CENTRALIZED  (CO

    Arizona               20.2           36.8         .55
    Connecticut           17.2           33.0         .52
    Delaware              13.7            7.7        1.00
    Kentucky              15.7           16.2         .97
    Maryland              14.6           14.0        1.00
    Memphis, TN            8.1            3.7        1.00
    Nashville, TN         24.5           25.4         .97
    New Jersey            26.1           27.8         .94
    Oregon                24.0           38.3         .63
    Washington, D.C.      18.4           13.4        1.00
    Washington            19.0           28.1         .68
    Wisconsin             15.3           19.3         .79


DECENTRALI ZED

  Computerized Analyzers  (DC)

    Alaska
      Fairbanks           19.4           22.7         .85
      Anchorage           15.7           24.7         .63
    California            27.7           28.7         .96
    Michigan              15.8           12.9        1.00
    New York**             5.1           33.4         .15
    Pennsylvania          17.6           19.5         .90

  Manual Analyzers  (DM)

    Georgia                6.6           25.0         .26
    Idaho                  9.8           16.9         .58
    Missouri               6.7           20.5         .33
    North Carolina         5.6           21.1         .27
    Nevada
      Clark County         9.5           29.4         .32
      Washoe County       ll.O           29.4         .37
    Utah
      Davis County         8.7           21.3         .41
      Salt Lake County    10.0           21.3         .47
    Virginia               2.3           15.6         .15
    FOP all model yea^S, including light duty trucks.



    New York's analyzers are only partially computerized

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     Table  One  lists  reported  failure  rates  from  the  most
recent year  available  for each program, expected  failure rates
and  the  ratio  of  reported   to  expected  failure  rates.   The
reported failure  rates  are provided to EPA in several ways:  as
lump sum failure rates for all model years, as failure rates  by
model year,  or as  failure rates  by model  year  group.   In  the
latter   two   instances,    national    default    registration
distributions were  used  to weight separate model  year or group
failure  rates  together  into  one  overall  failure  rate.   As  a
result,   the  reported  overall failure  rate  here  may  differ
slightly from  the  actual  overall  failure  rate  experienced  in
each  program.    EPA  does   not  have  available  to   it   the
distribution   of    vehicles   tested   to   make   more   precise
calculations.   The  expected   failure  rates  are  based   on  the
emission  standards  used  in  the  program  applied  to  the  1984
Louisville  I/M data base.  This data base was chosen because it
is  the  best   available  data base  for  this   purpose.    It
represents  the non-I/M  fleet  and  covers  light-duty cars  and
light-duty  trucks.   The  failure  rate ratio  is  the  reported
failure  rate divided by  the  expected  failure rate  (in  the few
cases where  this  yields  a number greater  than one,  the result
was rounded down to one).

     This   report   discusses   six  potential  causes  for  the
differences  in  reported failure  rates  among  I/M  programs  (see
Table Two)   and  cites  available  data  to  support  or  cast doubt
upon  them.   The  data  come  from  a  variety  of   sources:  audit
reports   conducted  under    the  National   Air   Audit   System
guidelines;  reported   I/M program  failure   rates,  emissions
scores  and  other  data;  random roadside  idle emission   surveys
conducted by EPA;  and, various contractor  studies conducted for
EPA.  Analysis  of  I/M program data  is limited  by the   program
design:   how data  is handled and  reported  to  EPA  limits  the
kinds  of  analyses  that  can  be  conducted.   Since   resource
constraints  naturally   exist,  contractor   studies   have  not
generally  involved  analyses   of  all  I/M  programs  but rather
selected  programs  that  are   representative   of   the  different
types  of   programs.   As  a  result,  the  findings  must  be
extrapolated to other programs of a similar type.
                            Table Two

     POTENTIAL CAUSES FOR LOWER THAN EXPECTED FAILURE RATES

                 1)    Quality control issues
                 2)    Better maintained fleet
                 3)    Standards and coverage differences
                 4)    Anticipatory maintenance
                 5)    Pre-inspection repair
                 6)    Improper inspection

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QUALITY CONTROL ISSUES

     Non-dispersive  infra-red  analyzers  are  used  in  all  I/M
programs  to  determine  emission  levels  of  hydrocarbons  and
carbon  monoxide  from  motor  vehicles  subject to  the  program
requirements.   Proper  calibration  of  emission   analyzers  is
essential for obtaining accurate  test  results.  Quality control
requirements vary  somewhat  from program to program  but  several
common elements exist:

     1)    Weekly calibration of analyzers
     2)    Low range calibration gas
     3)    Weekly leak  check
     4)    Periodic audit of analyzers by program  officials
     5)    Zero and span within one hour of each test

     In  programs  with  computerized  analyzers,   some  quality
control   functions   are   done   automatically   and   software
protection exists  that prevents use of  the  analyzer unless the
leak check  and gas  calibration check  have  been  conducted and
passed  within  the  past  seven  days.    The  analyzer  software
guides  the  inspector  through the  steps  necessary  to  complete
quality   control   checks   thereby  insuring   consistency  and
accuracy.  EPA audits  of  computerized  analyzer programs usually
show few analyzers failing quality control checks.

     In programs with manual  analyzers, quality control is done
manually and  it  is up to the  inspector  to insure that  it gets
done.    Typically,   programs   specify  that   the   weekly  gas
calibration  and  leak  check  take  place  each  Monday  morning.
Nothing  exists  to  prevent  use  of the analyzer  if  the quality
control  functions  are  not  performed  except  periodic  audits  by
program  officials.   EPA audits  have  shown  that  analyzers  in
manual  programs  are  frequently  out   of  calibration,  possess
leaks, or have  other problems that can severely compromise test
quality (e.g. clogged  filters).

     Quality  control  for  calibration  gas  is  accomplished two
ways  in  I/M  programs:  through  periodic  station   audits  and
calibration  gas  specifications.  Most  programs  conduct monthly
audits  and  the auditors  carry gas cylinders  to  check analyzer
accuracy.  This  also  accomplishes a  check  on calibration gas
accuracy  because   once  erroneous  calibration  is  eliminated
continued  failure  of  an  analyzer  usually  indicates   a  gas
problem.  Calibration gas specifications  are  fairly consistent
among  I/M programs.    Most  specify an accuracy  of  +  2%  and
require specific  concentrations (i.e.,  a zero blend tolerance)
of 1.6% CO and 600 ppm  HC in nitrogen.

     Quality  control   lapses  will  diminish  the   accuracy  of
emission scores  and,  to  some degree,  will  alter  the pass/fail
outcome.  The question is whether quality control  lapses  could
explain the  low reported failure  rates  experienced  in some I/M
programs. There are three sources  of data that will  help  answer
this question: audit data, idle survey data,  and operating data.

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     If analyzers are typically  reading  low or .have significant
leaks in the sample system, vehicles with  emission  scores  close
to standards may pass the  test when they should fail.   A review
of the gas audit results obtained during EPA  audits in Georgia,
North  Carolina,  Idaho  and  Missouri'1'  shows that,  on average,
analyzers were  2%  out  of  calibration  on  the  high  side.   This
means that vehicles that have emission scores within that range
will tend to incorrectly fail rather than incorrectly pass.

     An analysis of the Virginia I/M data  was  also  conducted to
determine the  impact  of increasing  all  emission scores by 5%.
Five percent was chosen because the  audit  data  showed  that  over
80%  of  the  emission  analyzers  checked   were  within  the  5%
tolerance;  it  is  also  the  audit  tolerance  used  in  most  I/M
programs.  The  overall  failure  rate for  the  1975  through  1984
vehicles  sampled  for  this  analysis  increased  from 3%  to  4.2%
when emission scores were increased  5%.

     Finally,   random,   roadside   idle   survey   data(2'   were
analyzed to determine the impact of  lowering  emission  scores by
100  ppm  HC and  1%  CO  to simulate the results which would have
been obtained if the analyzers were  severely  out of calibration
or had gross leaks in  the  sampling  system.   Table Three shows,
for all model years, the idle  survey failure  rates  and the idle
survey  failure  rates   with  the cushion  added.   Note  that  no
dramatic drop  in  failure  rates occurs as  a result  of  a cushion
and  that  survey failure rates with the cushion are still much
higher than reported rates  in manual I/M programs.   These three
analyses  show  that  low  reported failure rates are  not explained
by typical quality control deficiencies in manual I/M programs.
  STATE
Connecticut
Missouri
New Jersey
New York
North Carolina
Pennsylvania
Virginia
                           Table Three

                       POTENTIAL IMPACT OF
                  QUALITY CONTROL DEFICIENCIES
                      ON I/M FAILURE  RATES
PROGRAM
 TYPE

  CC
  DM
  CC
  DC
  DM
  DC
  DM
REPORTED
  FAIL
  RATE

 17.2%
  6.7%
 26.1%
  5.1%
  5.6%
 17.6%
  2.3%
SURVEY
 FAIL
 RATE
16
16
34
22
17
18
16
0%
2%
1%
2%
6%
4%
0%
SURVEY
 RATE
 WITH
GUSH I ON

13.2%
11.2%
27. 1%
19. 1%
14.7%
15.3%
13.5%

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BETTER MAINTAINED FLEET

     There   is   no    evidence   to   indicate   that   mechanic
effectiveness  varies  significantly  from  region to  region  or
that  vehicle  owners  are  more  conscientious   about   getting
repairs   in   one  state  or   another.    Nevertheless,    it   is
conceivable  that  better general  maintenance in  an area  could
result in a  cleaner  fleet,  overall,  and lower than expected I/M
failure rates.  If this  were  the case,  then there should be  a
significant  difference  between  failure rates of  non-I/M survey
vehicles  (i.e. those  registered  outside the  program boundaries
but  surveyed while operating within the I/M boundaries)  among
I/M areas.   Table  Four  illustrates  the survey failure  rates  at
constant cutpoints (pre-1981: 3.0%  CO/300  ppm HC; post-80: 1.2%
CO/220 ppm  HC) for  non-I/M  vehicles   in  two groups  of  areas
where  EPA has  conducted random,  roadside  idle surveys.   The
data  were grouped  due  to  the  small  sample size  of  non-I/M
vehicles  in  I/M  areas.    The  members  of  the   group  were
determined based  on  reported failure  rates,  VA,  et.al.  being
low and PA,  et.al. being high.   Note that the failure rates are
very  similar  between  the  two  groups.   This  indicates  that
maintenance  differences  between  areas  does not   seem  to  be
influential  on  non-I/M vehicle  failure rates.    By extension,
this  is  likely to be  true of  I/M  vehicles in  these  areas  as
well.
                           Table Four

                  NON  I/M  VEHICLE  FAILURE  RATES
               USING CONSISTENT  EMISSION STANDARDS

MODEL YEARS          VA,NC,MO,NY           PA,CT,NJ,OR
Post 1980
Pre-1981
Overall
7.1%
50.6%
20.7%
6.4%
54.7%
24.2%
EMISSION STANDARD AND VEHICLE COVERAGE DIFFERENCES

     Emission standards  are  established  by each program and are
used  to  determine  whether   a  vehicle  must  be  subjected  to
repairs to bring  emission  levels  down to acceptable levels.  No
two  I/M programs  have  identical  emission  standards  for  all
model years,  although most programs  use  the same standards for
1981  and  later   vehicles   (1.2%  CO  and   220ppm   HC) .    The
difference in standards from program to program should result
in different  failure rates.   Two  other  important  factors  that
contribute  to  expected differences  in  overall  failure  rates
among  programs  are  model   year   coverage  and  vehicle  type
coverage.   Thus,  it  is conceivable  that   low  failure  rates in
some I/M programs  may be due to  one  or  a  combination  of  these
factors.

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     To  evaluate  this  question,  the  emission  standards   and
vehicle  coverage  from  every  I/M  program were  applied  to  a
common data base consisting of  emission distributions from  the
Louisville  I/M  program'3'.   The  Louisville  program  data  is
for calendar  year  1984  and represents  the non-I/M fleet  since
the  Louisville  program  started  January  l,  1984.   Table  One
lists the  expected failure rates for  twenty-one  I/M  programs.
The expected  failure  rates range  from  a low of  3.7%  in Memphis,
Tennessee to a high of 38.3%  in  Portland,  Oregon.

     Expected failure rates for  most I/M programs fall  into  the
20% to  40%  range.   In particular,  the expected failure  rates
for manual  decentralized programs  also fall  into  this  range,
with  the  exception  of  Virginia and  Idaho which have  expected
failure rates  of 15.6% and  16.9%,  respectively.   It is  clear
that,   based  on  this  analysis,  the  combination  of  emission
standards,  vehicle coverage and  model year  coverage yields  a
range of  expected  failure  rates but  does not explain the  low
reported failure rates of decentralized manual I/M programs.

     To evaluate this question  further,  the ratios of  reported
failure rates to  the expected   failure  rates were  calculated.
The third  column in  Table One  shows the  ratio  of reported to
expected  failure rates, hereafter  referred to as failure  rate
fractions.  The  failure  rate  fractions in  manual decentralized
programs  are  all under  0.4,  except Idaho  and  Utah.   Most other
I/M programs have  failure fractions over  0.7.   Some  differences
in failure  rate fractions are anticipated  due  to variations in
historical  emission  standards,  tampering  program   coverage,
waiver rates,  pre-conditioning,  and length of  operation  of the
program.   For  example,  the emission  standards used  throughout
the life  of  the program are very  significant.  Note  in Table
One that,  among the  centralized programs, the  three with  the
lowest failure rate  fractions  also have  the  highest  expected
failure rates.  These programs have a  history of  tight emission
standards  which has  led  to  lower current failure  rates  than
otherwise expected.   The  failure rate  fractions  in  centralized
programs  reflect  normal   program  variations.   However,  these
variations  are  never  large  enough to  cause  the low  failure
rates  in  manual,   decentralized programs.   Thus,  the  normal
range of  failure rate fractions  also shows that  differences in
emission  standards  and  model year  coverage do not  explain low
failure rates in manual decentralized I/M programs.


ANTICIPATORY MAINTENANCE

     Anticipatory  maintenance  occurs  when  a motorist  obtains
repairs on  a  vehicle due to  have  an inspection  in  the  near
future with the  intent  of  avoiding test failure but without the
knowledge  that  the  vehicle  would,   in  fact,  fail  the  test.
There is  no doubt that anticipatory maintenance  occurs,  but it
is not clear  to  what extent  it  occurs  and there  is  no evidence
to show  it  occurs more frequently  in one program  or another.
It   is    conceivable   that   anticipatory  maintenance   could
contribute to low initial test failure rates.

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     Data   collected   by  EPA   indicate   that   anticipatory
maintenance may  not  achieve  its intended  goal;  in fact,  some
evidence  indicates  it  will  increase chances  of test  failure!
In  1979  and  1980,  EPA conducted  several  studies(4•s • *}   to
determine  the  potential  emission  benefits  of   a  mandatory
vehicle maintenance program.   In these  studies, mechanics  were
asked to  adjust  vehicles to  manufacturer  specifications.   The
mechanics were not  aware that they  were being  tested; some of
the  vehicles  were  out  of  adjustment   (i.e.,   Federal   Test
Procedure  (FTP)   failures)   and   some  were not.   In  Houston,
adjustment by mechanics resulted  in a 2.3%  increase  in FTP  mass
HC  emissions  and  a  2.7% increase in FTP  mass  CO emissions.   In
a  St.   Louis  study,  an  87%  increase  in  idle  HC  and  a  30%
increase  in  idle CO emissions were  observed  in 83 shops.   In
general,  vehicles  that  were  FTP  failures  before  "repairs"
showed  some  reduction  in emissions  while  clean cars  typically
suffered  emission  increases.   The two  situations   studied  here
are  analogous  to that  of anticipatory  maintenance:   mechanics
are not  being asked  to fix  the vehicle  in response  to  an  I/M
failure.

     Even  when repairs occur in  response  to an  I/M failure,
successful emission  reductions are not  assured.   Retest  failure
rates in  centralized I/M programs  are  typically  in  the  30-40%
range.   Thus,  it   is  reasonable to  assume  that  anticipatory
maintenance may not be  very  successful,  especially when applied
to  "clean" cars.

     Another reason anticipatory maintenance  does  not seem to
be  a   satisfactory  explanation  for   low  failure  rates  in
decentralized  manual   I/M  programs  is  that  its  effect,  if
important, should  be felt in  other  types  of  programs as well.
In  fact,  it  is arguable that  the effect  should be greater in
centralized programs.    In decentralized programs,  the  testing
and  repair  functions   are  combined  in   the  service  station
environment.   The  normal sequence of  events  is for  a customer
to  visit  the  garage,  get an  emissions  test  and  if  a  failure
occurs,    to  obtain  repairs   at  that  facility.    Given   this
scenario, there is  little motive for  anticipatory maintenance.
In  centralized programs,  where  repair  functions  are separate
from testing functions,  the motorist has  to make at least three
trips  if an  initial  test  failure  occurs.   This  provides an
incentive  to   avoid  initial  test  failure especially  when   a
failure  was  experienced in  a  previous  year.   Data from  the
Arizona  and  Seattle,   Washington  I/M  programs  show  that,  in
those   centralized   programs,   vehicles   that  failed  in  the
previous  year fail  at higher  than average  rates.   This  implies
that  motorists  are not  attempting  to  avoid  failures  through
anticipatory   maintenance   or   that   such   maintenance   is
unsuccessful.

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PRE-INSPECT I ON REPAIR

     Pre-inspection repair may occur  in  two  basic ways:  first,
a vehicle  is  brought  in for an emission test and  an  unofficial
initial  test  is  conducted to  determine  if  the  vehicle  will
pass.   If  the vehicle  fails,  repairs  are conducted such that  it'
will  pass  and  then  the  official  initial test  follows.  The
second  scenario  occurs when  a vehicle  is  brought  in  for  a
tune-up plus  an emission test  and,  again, repairs precede the
official test.    It  is likely  that  this  phenomenon  occurs  in
both  manual  and  computerized  decentralized   I/M  programs.   To
the extent  that this  is  the  case,  it would  lower  initial  test
failure  rates  but  is   a  phenomenon  confined to  decentralized
programs.

     The structure  and requirements  in  manual  and computerized
decentralized  I/M  programs  are   essentially  the   same  with
exception  of   the  analyzer.   Thus,   the   opportunities   and
incentives  for  pre-repair  are about  the  same in both  types  of
program.   So,   the  impact  of pre-repair  in terms  of  failure
rates should be  the  same  as well.  However,  the data  in Table
One  show  that  decentralized programs  with  fully  computerized
analyzers  do  not  experience  the  very   low  failure  rates  of
decentralized    manual   programs.     This     indicates    that
pre-inspection  repair  does  not  seem  to be  a  big  issue  in
computerized  programs.   The  software prompts  in the  Michigan
computerized  analyzer  include  a  question  regarding  repairs
within  a week preceding  the  initial  test. The  data*  }  for the
first  and   second  quarters  of  1986  show  that  for  vehicles
passing  the  initial  test,  about   6-7%  were  known  to  have
received repairs within  the  past  week.   It is  not known how
many  of these  vehicles  received  anticipatory maintenance  as
opposed  to pre-inspection repair.  It  is also  unknown whether
the  repairs  were  actually needed  to pass the  initial  test  or
effective  at  reducing  emissions.    In  any  case,  these  data
indicate   that,   taken  together,  pre-inspection   repair  and
anticipatory maintenance  are  not  common  phenomena and therefore
are  not  satisfactory  explanations  for  low  reported   failure
rates.
IMPROPER INSPECTION

     Improper  inspection  is believed  to  occur  in several ways:
inspectors skip  the  emissions  test and invent  passing emission
scores;  inspectors  conduct  the test  but still  invent passing
emission scores  without doing  repairs;   inspectors  conduct the
test,  do  repairs  but,  failing  to  bring  the  vehicle  into
compliance,  they  invent   passing  emission  scores.    We  can
imagine  a  host  of  variations  on these  three  basic scenarios,
but  all   have  one   important  factor  in   common:   emission
reductions  are  not  achieved.    Improper  inspection   has  been
found to occur  in  I/M programs through covert  audits.   What is
unclear is the magnitude of the problem.

-------
     One  way to  assess  whether  emission  reductions  are  not
actually occurring  is  to randomly test  I/M vehicles.  EPA  has
conducted random  idle surveys  throughout  the country.   Figure
One, on the next page,  illustrates the results for  four  eastern
states: North Carolina, Connecticut, Pennsylvania  and Virginia.
The graph lines  illustrate  the failure  rate  of  1981  and  later
vehicles  (using  each program's own  outpoints)  over  time  since
last inspection.  Note  that the  rates  for  North Carolina  and
Virginia are relatively  flat -  the failure  rates start high and
end  high.   On   the  other   hand,   the   failure   rates   for
Pennsylvania  and  Connecticut   start  low   and  end  high.   The
symbols on the right Y  axis are the reported failure rates  for
the four  programs.   The reported  failure rates  in Pennsylvania
and  Connecticut  are essentially  identical  to  the  12  month
survey  failure   rates.   The  reported  rates for  Virginia  and
North  Carolina  are  much  lower  than  the  survey  rates.   Two
things  are  apparent from  these  results:   vehicles  sampled in
the survey in Virginia  and  North  Carolina  do not seem  to  have
their  emissions  lowered after  inspection,  and  the  reported
failure  rates   do  not  accurately  reflect  the  actual  idle
emissions of sampled vehicles.

     The  data  presented  in Table  Five  show  a comparison of
overall  I/M  failure rates  and  reported  failure rates  for  cars
in  the   same   four   states.    The  reported  failure  rate  in
Pennsylvania and Connecticut  is  very  similar  to   the  survey
failure  rate.   In  North Carolina  and  Virginia,  the reported
failure  rates  are  much  lower   than  the survey  failure  rates.
Again,  the  data  indicate  that  I/M  cars  are  not  achieving
significant emission reductions.
                           Table Five

                     REPORTED FAILURE RATES
                    VS. SURVEY FAILURE RATES


STATE
North Carolina
Virginia
Pennsylvania
Connecticut

PROGRAM
TYPE
DM
DM
DC
CC


REPORTED
5.6%
2.3%
17.8%
17.2%
LOCAL
SURVEY
I/M CARS
17.8%
14.9%
18.2%
16.4%
     An EPA  contractor was given  a work  assignment  in 1986 to
analyze   I/M  program  data   to   determine    if   significant
differences  were  present  between  different I/M programs.  The
draft  report'8)   from  this  work  has   been   completed.   The
contractor   analyzed   and  compared   reported   data  from  I/M
programs  in  Washington,  Virginia,   New  York,  Colorado  and
Connecticut  (an  analysis  of  Massachusetts data  is  in progress
and will be available  in early 1987).
                               10

-------
               Figure One
  Comparison of Idle Surveys
1981 And Newer Passenger Cars
                                          Site
                                         O CT ©
                                         a PA ffl
                                         o NC »
                                         A VA A
  23456789
     Months Since Last Inspection
11  12
Survey   Reported

-------
     Three  representative model  years were  analyzed to reduce
 the  enormity of the  task:  1977, 1980  and 1982.   Manufacturers
 were broken  up into  three groups:   Gl   consists  of Chrysler,
 Ford and  AMC; G2 consists of only GM vehicles;  and, G3 consists
 of   all   imports.   Figure  Two  provides   an  example frequency
 distribution of   carbon  monoxide   failure  rates  from  four
 programs.   The  X  axis  is the CO emission score  and the Y axis
 is the percent  with that  score  in the sample.  The Virginia and
 Colorado  data  are very  "spiky"  since the emission readings are
 manually   recorded and   mechanics   appear  to   round  off  the
 readings.   Note  also that  the  decentralized  programs  show  a
 step change in   the  distributions  exactly  at  the  program
 cutpoints.   Additionally,  the  distributions  show  a very  large
 number  of  vehicles  (relative  to  Washington)   just  below the
 cutpoints.   This  distribution  is typical of what  was found  in
 other model  years,  in other vehicle  groups, and for  HC.

     The  contractor  also  produced  cumulative  distributions  to
 overcome  the distortion created  by  the spikes  in the data.   In
 these  curves,  the gradient of the curve  is proportional to the
 number  of vehicles at  the  particular emission level.  Figure 3
 shows  the   distinctive   kink   in the curves  at   the  program
 cutpoints   except    in   Washington,   which   shows   a  smooth
 distribution.   To  further  assess the question  of whether the
"kink in  the  curves  could be due to pre-repair, the contractor
 compared  the first test  results  from the  decentralized  programs
 with two  sets of  results:

     1)     the  first  test  results of centralized  programs.
     2)     the  first  test  results  of  centralized  programs  for
            passing vehicles  and the  retest  results  for failed
            vehicles combined  into one distribution.

 The  latter comparison is intended to simulate the  distribution
 resulting  from  pre-repair.    Figures 4  through   9  show  the
 results  of these analyses.   The  contractor's analysis of  these
 figures  succinctly states the case:

     From Figure  Five it is obvious  that Connecticut's  vehicles
     have much  lower  emissions after  repair, and the New  York,
     Colorado   and  Virginia curves  lie between  the  Connecticut
     first  test  (Figure Four)  and   after  repair  distributions
     (Figure Five) .  This  may  indicate  that a  fraction of  the
     cars are  being pre-repaired   and  that  the  average   lies
     between   the  two   Connecticut  distributions.    However,
     examination  of the  1980  and 1982 distributions  in  Figures
     Six  to Nine  shows  that this hypothesis is unlikely  to  be
     correct.   For both  model  years  the Connecticut's  initial
     test  distributions   shows   lower   CO   values  than  the
     decentralized   program    distributions.     Moreover,    a
     substantial    portion   of   the   population   in    each
     decentralized program  appears  to have  CO  emissions  just
     below  the  cutpoint  (as  indicated by the  steepness of  the
     line just below the  CO  cutpoint).    It is  [usually]  not
     possible  to   repair  1980  and 1982 cars  to  "just meet"  the

                                12

-------
20.00 -r
18.00
16.00
14.00 4-
12.00 4-
10.00 4-
 8.00 +
 6.00 4-
4.00  4-
2.00  4-
o.oo
                                     Figure Two
               FREQUENCY DISTRIBUTION OF REPORTED EMISSION SCORES
             IN THREE DECENTRALIZED  AND ONE CENTRALIZED  I/M PROGRAM
                      Carbon Monoxide Frequency Distribution
WA'77 G1
NY77G1
VA'77G1
CO77G1
    0-50   1.50   2.50   3.50   4.50   5.50   6.50  7.50   8.50   9.50  10.50  11.50
                                     Encfeoint

-------
100.00 -r
 95.00 +
 90.00 4-
85.00 4-
                                      Figure Three

               CUMULATIVE DISTRIBUTION OF REPORTED EMISSION  SCORES

                        Cumulative CO  Frequency Distribution
                                                               ^ Cumulative sum of
                                                                -  WA77G2

                                                               •"• Cumulative sum of
                                                                  NY77G2

                                                               ••• Cumulative sum of
                                                                  VA77G2

                                                               esss* Cumulative sum of
                                                                  CO77G2
           1.50   2.50   3.50
4.50   5.50

  BCPCNT
6.50   7.50    8.50   9.50   10.50  11.50

-------
100,00 -r
 98.00 4-
 96.00 4-
94.00 4-
92.00 4-
90.00 4-
88.00  4»
86.00
                                      Figure Four


                    INITIAL TEST  SCORES CUMULATIVE  DISTRIBUTION
                                    MODEL YEAR  1977
                        Cumulative  CO Frequency Distribution
                                 NY

                        CO
•" Cumulative sum of
   CT77G2

•« Cumulative sum of
   NY77G2

"•• Cumulative sum of
   CO77G2

*"" Cumulative sum of
   VA77G2
                                      I    I   I   I   I   I    I   I   I   I   I  I   I   I

     0.50   1.50   2.50   3.50   450   5.50   6.50   7.50   8.50   9.50  10.50   11.50

-------
                                   Figure  Five
PERCENT
  100 -r
                       CONNECTICUT  RETEST  SCORES VERSUS
                INITIAL TEST SCORES  IN  DECENTRALIZED PROGRAMS
                                 MODEL  YEAR 1977

                     Cumulative CO  Frequency Distribution
   98 4-
   96 4-
   94 +
   92 +
   90 +
   88 +
86
                             *     i   i   i
                                                          — CT77COG2-
                                                            MCQOJM.

                                                          «««• Cumulative sum of
                                                            NY77G2

                                                          •"•• Cumulative sum of
                                                            CO77G2

                                                          SSM* Cumulative sum of
                                                            VA77G2
I   I   I   I   I   I   I    I   I   I   I
      0.5    1.5   2.5    3.5    4.5    5.5    6.5    7.5    8.5    9.5   10.5    11.5

-------
                                     Figure Six
   PERCENT
100.00 -r
                   INITIAL  TEST SCORES CUMULATIVE DISTRIBUTION
                                  MODEL YEAR 1980
Cumulative  CO Frequency Distribution
 98.00 -•
 96.00 -•
 94.00 - -
 92.00
90.00 -•
                                                                          Cumulative
                                                                          sum of CT
                                                                          '80 G2

                                                                          Cumulative
                                                                          sum of NY
                                                                          '80 G2  .
88.00
                                                                          Cumulative
                                                                          sum of CO
                                                                          '80 G2


                                                                          Cumulative
                                                                          sum of VA
                                                                          '80 G2
                                                                 I   I  I   I   I  I
     0.25  0.75  1.25  1.75  2.25  2.75  3.25  3.75  4.25  4.75  5.25  5.75  6.25  6.75
                                       BCPONT

-------
                                  Figure  Seven
 PEFCBJT
100 -r
                       CONNECTICUT RETEST SCORES  VERSUS
                INITIAL  TEST SCORES IN DECENTRALIZED  PROGRAMS
                                MODEL YEAR  1980

                     Cumulative CO Frequency Distribution
 98 - -
 96 -•
 94 -•
 92
 90 --
 88
                                                                      CT'SOCO
                                                                      G2-
                                                                      MOO.CIM


                                                                      Cumulative
                                                                      sum of NY
                                                                      '80 G2
                                                                      Cumulative
                                                                      sum of CO
                                                                      '80 G2

                                                                      Cumulative
                                                                      sumofVA
                                                                      '80 G2
   0.25  0.75  1.25  1.75  2.25  2.75
3.25  3.75
  BCPONT
4.25  4.75  5.25  5.75 6.25  6.75

-------
                                    Figure Eight
PERCENT
100.00 -
INITIAL TEST  SCORES CUMULATIVE DISTRIBUTION
                MODEL YEAR 1982


    Cumulative CO  Frequency Distribution
 98.00 --
 96.00 --
 94.00 --
 92.00  --
 90.00  -•
 88.00
                                              Cumulative sum of
                                              CT82CQG2

                                              Cumulative sum of
                                              NV82COG2

                                              Cumulative sum of
                                              C082COG2

                                              Cumulative sum of
                                              VAB2COG2
0.125     0.625      1.125     1625     2.125

                                  BCPCNT
2.625
                                          3.125
                                                                      3.625

-------
 PERCENT
 100.00
                        Figure Nine

            CONNECTICUT RETEST  SCORES  VERSUS
     INITIAL  TEST  SCORES  IN DECENTRALIZED  PROGRAMS
                      MODEL  YEAR 1982

          Cumulative CO Frequency Distribution
 98.00
 96.00 4-
                                                              "CTB2COG2-
                                                                 MCO.OJM.

                                                              ™" Cumulative sum of
                                                                 NYB2COG2
 94.00 4-
 92.00 4-
90.00
88.00
                                                              *•• Cumulative sum of
                                                                 COB2COG2

                                                              «•«. Cumulative sum of
                                                                 VAB2COG2
    0.125
0.625
I  I   I  I  I   I  I  I  I  I  I  I

1.125    1.625     2.125
                B^DPONT
                                                  2.625
                                              3.125
3.625

-------
     CO  standard  as  they  have  sealed  adjustments   for   the
     carburetor  and,  in  many cases,  sophisticated electronic
     controls  that  are  either  operational  or malfunctioning,
     with no  "in-between" states.   Finally,  it  must  be noted
     that  Connecticut's   post-repair  distribution  for  any  of
     three model  years considered do not show a  large  group of
     cars below  the  applicable  cutpoint.   This   indicated  that
     even  if  some form  of pre-repair  occurs in  decentralized
     programs,  the  repairs  do  not  appear  to be  complete  or
     related to the defects in the emission control  system.

     Two  important  conclusions  drawn from  this  data  analysis
are that:

     1)  Emission distributions  from  decentralized programs  do
not resemble  those from  centralized  programs.   In decentralized
programs the  distributions exhibit a distinctive discontinuity
at program cutpoints.

     2)  Pre-repair  is not a  satisfactory  explanation  for  the
shape  of the emission  distribution curves,  particularly  for
newer model years, in decentralized programs.

     The contractor  devised  two indices to attempt to further
distinguish  pre-repair  and  improper  inspection.    The  abnormal
freguency (ABF)  index  is  the percent of cars that have emission
scores 0.7 to 1.0 times  the  cutpoint.   This  index is  based on
the finding that  an  abnormally large percentage of the fleet in
decentralized programs is reported to have  emission scores  in a
narrow emissions  range just  below the cutpoints.   The index for
Washington and Connecticut was  approximately  0.15  for  1975 to
1980 vehicles using  the 3.0%  CO/300  ppm HC cutpoints,  dropping
to  0.09  using 6.0% CO/600  ppm HC cutpoints.   Analysis  of  New
York, Colorado  and Virginia  data showed that 40-60% of stations
analyzed have high ABF indices.

     The  second  index   devised  is  the  repeat   index.    In
reviewing  I/M  station  records,  EPA  auditors  have  observed
reported emission scores repeated again and  again at  a given
station.  The contractor  devised a repeat index by counting the
number of times  each HC  and CO  value is  repeated and  the three
highest  counts  for  HC  and  CO  (six in  all)  are  added.   This
number  is  then  normalized by  the  sample  size  to derive  the
Repeat  Index.   By way of  example, if all  emission readings are
reported as one  of  any three values  for  HC and  CO (an extreme
case),   the   calculated   Repeat   Index   would equal   2.   The
contractor  suggests  a  level  of  0.5  to  0.6 as   criteria  for
identifying  potentially   dishonest  stations.   Analysis  of  the
Colorado and  Virginia  data  showed indices  ranging as  high as
1.2.   Figures Ten  and  Eleven  illustrate  the results  for  all
stations examined.
                               21

-------
       Figure Ten



REPEAT INDEX  - COLORADO



  Frequency Bar Chart
MIDPOINT
RPT.IDX
0.025
0.075
0.125
0.175
0.223
0.275
0.325
0.375
0.4Z5
0.475
0.525
0.575
0.625
0.675
0.725
0.775
0.825
0.875
0.923
0.973
1.025
1.075
1.125
1.175
FREQ
0
0
1
•••••* 16
••••••.•••••••••••ft.** 56
•••••••»•»•••••••••••••••••€••• 78
••••••••••••••••••••••••••••••••I*** 91
	 152
	 170
	 	 	 195
	 	 	 164
••••••••••••••••••••••••••••»« »**•**•»«•*•*»•*•**••»* 132
•••••••••••••••••••••••t***»*« **»**•«**•*****»** 119
	 • 	 	 70
70
*«••*•«•«§*••*»**•* 47
*•*••***•**** 32
••••••*** 23
• •## 10
• •*• 9
** 6
• • 3
• 3
• * 4
	 4 	 * 	 » 	 » 	 * 	 •» 	 « 	 * 	 * 	 * 	 * 	 4 	 +___*__-* 	 .«-..>. 	 4.---*--
CUtt.
FREQ
0
0
1
17
73
151
242
394
564
759
923
1055
1174
1244
1314
1361
1393
1416
1426
1433
1441
1446
1449
1453
PERCENT
0.00
0.00
0.07
1.10
3.85
5.37
6.26
10.46
11.70
13.42
11.29
9.08
8.19
4.82
4.SC
3.23
2.20
1.58
0.69
0.62
0.41
0.34
0.21
0.28
CUM.
PERCENT
0.00
0.00
0.07
1.17
3.02
10.39
16.66
27.12
38.82
52.24
63.32
72.61
80.80
85.62
90.43
93.67
95.87
97.45
98.14
98.76
99.17
99.32
99.72
100.00
 FREQUENCY

-------
     Figure Eleven



REPEAT INDEX - VIRGINIA
MIDPOINT
RPT_IDX
0.025
0.075
0.125
0.175
0.225
0.275
0.325
0.375
0.4T3
0.47b
0.525
0.575
0.625
0.675
0.725
0.775
0.825
0.875
0.925
0.975
1.025
1.075
1.125
1.175

Frequency Bar Chart FREO
0
0
****** 3
»•*••**•»**• 6
«*** 	 •••* 10
.... 2
**•••«*••*•»*• 7
••••••••••••i* 7
	 	 	 22
•••••••••••I***...***********.**.***.*********.*** 25
«•*•««•»*•**•••••••***••••*•**•***•**•**••***•*•*» 25
	 	 	 	 	 31
•••«.••••«••«*••***••*•*»•******•*•*•*.«*«•*••«•*•• 25
	 * 	 * 	 35
	 .........*.*. 	 	 	 33
••••A*******************.****..**... 16
••*••**••**•***••***»•••*•*•»•**•*«***»* 20
•**»•*****•***•»****.* II
**••*•**••*»••••*« 9
•••••******* . 6
A******************* 10
•**••*•» 4
**»• 2
•••••••ft************ 10
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34
CUM.
FREQ
0
0
3
9
19
21
28
35
57
82
107
138
163
198
231
:47
269
280
289
295
305
309
311
321

PERCENT
0.00
0.00
0.93
1.87
3.12
0.62
2.18
2.18
6.85
7.79
7.79
9.66
7.79
10.90
10. re
V6I
6.23
3.43
2.80
1.87
3.12
1.25
0.62
3.12

CUM.
PERCENT
0.00
0.00
0.93
Z.80
5.92
6.54
8.72
10.90
17.76
25.55
33.33
42.99
50.78
61.68
71.96
77.97
83.80
87.23
90.03
91.90
95.02
96.26
96.88
100.00

  FREQUENCY

-------
     In addition to the  extensive  data analysis just  reviewed,
practical experience  in the  field leaves  a strong  impression
that improper inspection is occurring.   The procedures used  by
EPA  to  audit  I/M  programs  include  a  review  of  records  and
usually   a   demonstration   by   station   inspectors   of   test
procedures.   Auditors have  often witnessed a lack  of  knowledge,
on  the part of  inspectors,  of  how  to correctly  conduct  the
emission test or calibrate  the  instrument.  Unfortunately,  most
decentralized  programs   have   limited   resources  and   place
insufficient emphasis  on identifying  stations  that  improperly
inspect  vehicles.   Nevertheless,  there  are  examples  in  every
manual  I/M  program  where  undercover  work  is   conducted  of
inspectors who got  caught   issuing  a  certificate  of  compliance
without conducting the emission test.

     There  are  plenty  of   reasons why  it  is  advantageous  to
improperly  inspect  rather  than conduct   inspections  properly.
Mechanic/  inspectors  are  in   business   to  make  money,   not
necessarily  to  reduce  air  pollution.   Customers  want to  meet
the program  requirements with as little time and expenditure as
possible.  If a regular  customer goes  in  for an emission test,
there  is  strong  incentive  for the  mechanic  to  report  a  passing
result  regardless of  actual  performance of the vehicle.   If a
tune-up  had  been  done  recently  on  the  vehicle,  reporting  a
failure  would  call   into question  the  mechanic's  ability  and
trustworthiness.   There  is  also  the situation where the station
does not  have  any competent  mechanics.   These stations  are in
the business  to  collect the  test   fee  and sell gasoline.  They
fail  a  few  vehicles  here  and   there   to  avoid  attracting
suspicion.   Undercover  work  has   verified  that   such  stations
exist;  customers come to realize  that  they will  always  pass at
this station, word spreads and business is up.

     On  the  other  hand, there  is the  competent  mechanic  who
runs  a  good  station  and   is faced with   increasingly  complex
automotive  systems  for  which  training is  difficult  and  time
consuming to  obtain.   This mechanic may  try very hard  to  fix
the vehicle  but  in failing  to  do  so  may  report  a pass anyway.
To  say  the  least, customers  would not be very happy with  the
mechanic  charging  them the  test  fee,   repair  costs and  not
getting the vehicle to pass!  Good  mechanics are  in high demand
these  days,  and when  the  workload is  heavy,  they may  issue a
certificate without  doing  the  test,  simply  for  lack of time.
In  that failure  rates  among new   technology vehicles  are  very
low, some  mechanic/inspectors  may come   to  believe   that  they
never  fail  so they  don't   bother   testing  them after  a  while.
These  motives  and  incentives are  very  real  problems.   While
there  is  little in  the way  of  hard  evidence  to  support these
ideas,   they  are  based  on  observations   and  discussions  with
mechanics and program officials during audits.
                               24

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CONCLUSIONS

     This  paper  has reviewed  a variety  of possible causes  for
the  low  reported  failure  rates found  in  manual,  decentralized
I/M  programs.   It  was  shown  that  quality  control  lapses  could,
at  most,  only  make  a  small  difference  in   the  failure  rate
outcome  in  an  I/M  program.   Another  potential  explanation,
better  local   maintenance,  does not  seem  to  be  the  case  when
emission  scores of  non-I/M  vehicles   from different  areas  are
analyzed using  uniform cutpoints.  Cars in  one  area seem to fail
at   rates  similar  to   that  of  other  areas.    Differences  in
emission standards  and model  year  coverage  among I/M programs do
not  seem  to   explain   the  low  reported   failure  rates.   By
estimating the  expected  failure rates  using a  common  data base,
it   was   shown   that   reported   failure   rates   in   manual,
decentralized   I/M  programs   differ   radically   from   expected
failure  rates.   This was not  the  case  for  other  program types.
Anticipatory maintenance and  pre-inspection repair, while likely
to  exist  to  some  limited  extent,  do  not  seem to  be  prevalent
phenomena  and  therefore cannot  contribute  significantly  to low
failure  rates.

     Improper  inspection seems  to  be  the  primary cause  of' low
failure  rates   in  decentralized,   manual   I/M  programs.   The
random,  roadside  idle  surveys  conducted by EPA  show  that I/M
vehicles  are  not   "clean"  after  inspection.   The  surveys show
that failure  rates  of  these  vehicles  are  much higher  than the
reported  failure  rates.   Analysis of  I/M  data shows  that the
reported emission  scores from manual  decentralized I/M programs
are  very  unusual.   Instead of  having  a smooth  distribution of
emission  scores,  the  distributions  from  manual   programs show
higher  scores   close  to  the  cutpoints.  A  distinct kink  in the
distribution occurs  right  at  the cutpoint,  where the scores drop
off  dramatically.   This   indicates   that   inspectors  are  not
entering real  test scores;   the  scores  they  invent  are more
often  right  below the  cutpoint.  The  abnormal  frequency  index
attempts  to  quantify  this phenomenon  and  shows   that  in  fact,
there  are unusually  large numbers  of  vehicles just  below the
cutpoints  in   these  programs.   Patterns  of  emission  scores have
been observed  during  audits  of  inspection stations   in which
scores   are  repeated  again  and again.   The  repeat   index was
devised  to quantify  this   in  relation  to  the  cutpoints  and it
lends  support   to the  observed  pattern  of  emission   scores.
Finally,  practical experience  during  the  audit process  and the
results   of   undercover   inspection  work   show  that  improper
inspection is  a  problem.

     In  conclusion,  low   reported   failure  rates  in   manual,
decentralized  I/M  programs seem to be primarily  a  function of
improper   inspection rather  than  simple  variations  in   vehicle
coverage,  test procedures   or other  program characteristics.  In
order  to  correct   this  problem,  programs  will  have   to   either
eliminate  the  manual  aspect  of the  program  or  put  sufficient
resources  into  oversight and undercover efforts.

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REFERENCES
1    National  Air  Audit  System  Reports.   1984,  1985,   1986.
     U.S. EPA, Office of Air Quality Planning and Standards.


2    Motor Vehicle  Tampering Surveys - 1983,  1984,  1985,  1986.
     U.S. EPA, Office of Air and Radiation.


3    Vehicle Exhaust Testing  1984 Annual  Report,   Air Pollution
     Control District of Jefferson County, January 25, 1985.


4    "Summary  of  Programs  Simulating  a  Mandatory  Maintenance
     Program."  Memorandum  from R.  Bruce  Michael, I/M  Staff  to
     Tom  Cackette,  Chief,  I/M  Staff.  U.S.  EPA, February  27,
     1980.
     Effectiveness  of  Idle Adjustment  on Light Duty  Trucks at
     Commercial Repair  Facilities.   John C.  Shelton,   Test  and
     Evaluation Branch, U.S. EPA, June 1980, EPA-AA-TEB-81-17.
     Effectiveness  of  Idle  Adjustment  on  Passenger  Cars  at
     Commercial  Repair  Facilities.   John  C. Shelton,  Test and
     Evaluation Branch,  U.S. EPA, October 1980,  EPA-AA-TEB-81-5.
7    Auto  Exhaust  Testing  Inspection  Statistics"  First  and
     Second Quarters 1986.  Michigan Department of State.


8    Development of Data  Analysis  Systems  for Decentralized I/M
     Programs, Energy  and Environmental  Analysis,  Draft Report,
     September 1986.
                               26

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