United States
                     Environmental Protection
                     Agency
Industrial Environmental
Research Laboratory
Research Triangle Park NC 27711
                     Research and Development
EPA-600/S7-84-077  Aug. 1984
v>ERA          Project  Summary
                     Paved  Road  Paniculate
                     Emissions  --  Source Category
                     Report

                     Chatten Cowherd, Jr. and Phillip J. Englehart
                       This study entailed an extensive field
                     testing program to develop emission
                     factors for particulate  emissions
                     generated  by traffic entrainment  of
                     paved road surface particulate matter.
                     The emission sampling procedure used
                     in this  program provided emission
                     factors for the  following particle size
                     ranges:  £ 30,  15,  10,  and 2.5 fan
                     aerodynamic diameter. Testing was
                     performed at sites in the Kansas City*
                     and St. Louis (MO) areas. These sites
                     represented  significant urban  paved
                     road emission sources in the following
                     land  use   categories:  commercial/
                     industrial, commercial/residential.
                     expressway, and rural town.
                       The measured inhalable  particulate
                     (IP--^ 15 fan aerodynamic diameter)
                     emission factors ranged from 0.06 to
                     8.8  g/VKT  (vehicle  km   traveled).
                     Lowest emissions were measured for
                     the  expressway category:  highest
                     emissions were measured for the rural
                     town category- About 90% of the IP
                     emissions consisted of particles £ 10
                     fan in aerodynamic diameter, and about
                     50% of the  IP emissions consisted of
                     particles <,  2.5 /urn  in aerodynamic
                     diameter.
                       Correlation analysis of  particulate
                     emissions with parameters characteriz-
                     ing the source conditions showed the
                     existence of a relatively strong positive
                     relationship  between  intensity  of
                     emissions and  roadway surface silt
                     loading. This relationship was used as
                     the  basis  for deriving  predictive
                     emission factors for each particle size
range. The equation for IP emissions
was found to represent measured IP
more accurately over a much larger
range of values than does the AP-42*
single-valued factor.
  To facilitate the use of these particle
size specific equations in  developing
emission  inventories, a classification
system of mean or typical silt loadings
as a function of roadway category was
derived. These mean silt loadings were
then  inserted  into  the  respective
emission  factor equations  to derive a
matrix of emission factors  for specific
roadway  categories and particle size
fractions.
  This Project Summary was developed
by EPA's Industrial Environmental Re-
search laboratory. Research  Triangle
Park, NC, to announce key findings of
the research project that is fully docu-
mented in a separate report of the same
title (see Project Report ordering infor-
mation at back).

Introduction
  As early as 1976, receptor oriented air
quality  assessments showed  traffic-
entrained  particulate from paved roads to
be a major cause of non-attainment of air
quality  standards  for total  suspended
participates (TSP)   in  urban  areas.
However,  only a few field programs (all
completed by 1977) had tried to directly
measure  dust emissions from urban
streets. Moreover, these programs were
seriously limited in the measurement of
aerodynamic particle size characteristics.
                    "In this Summary, failure to specify either Kansas or
                     Missouri after "Kansas City," implies both cities.
*U.S. EPA report AP-42, Compilation of Air Pollutant
 Emission Factors. Third Edition (NTIS PB 275525)
July 1977.

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  This study  was aimed at  developing
size-specific paniculate emission factors
for   urban  paved  roads,   based  on
expanded field testing of representative
sources. The  resulting emission factors
would provide for the development of
effective  strategies  for  the  attainment
and maintenance of the TSP standards,
as well as the anticipated standard for
particles  £10  yum  in  aerodynamic
diameter.
  The emission sampling procedure used
in this program provided emission factors
for the following particle size ranges:

  TSP = Total  suspended  paniculate
          matter (as  measured  by a
          standard  'high-volumesamp-
          ler) consisting of  particles <.
          30 yum in aerodynamic diame-
          ter.

    IP = Inhalable  paniculate   matter
          consisting of particles £15
          fjm in aerodynamic diameter.

PM-10 =  Paniculate matter consisting
           of  panicles £10 >um  in
           aerodynamic diameter.

   FP = Fine paniculate matter consist-
           ing of particles  £ 2.5 yum  in
           aerodynamic diameter.

Results are presented for winter testing
in the Kansas City, MO, area and spring
testing in areas of  St. Louis, MO, and
Granite City, IL

Sampling Site Selector
  Eight candidate  sampling  areas  in
Kansas,  Missouri,  and Illinois  were
identified  by  the  EPA  as  likely
representative sites for  the field  study.
These  areas represented a  range of
typical road,  traffic,  geographical, and
environmental conditions within residen-
tial, commercial, and industrial land uses.
Each  sampling  area contained  a TSP
monitoring site providing  historical air
quality data.
  Three major  criteria were  used to
determine the suitability of specific sites
within the designated areas, for sampling
of road dust emissions by the exposure
profiling technique:

  1.  Adequate space for sampling equip-
       ment.

  2.  Sufficient  traffic  and/or surface
       loading so that  adequate mass
       would be captured on the lightest
       loaded collection substrate during
       a reasonably short sampling time.
  3.  A wide  range of acceptable wind
       directions, taking into account (a)
       the street orientation relative to
       the predominant wind directions
       for the locality, and  (b) upwind
       obstacles (houses,  buildings, or
       trees) to free wind flow.

Although roads with light traffic were not
considered,  they probably  do   not
contribute   substantially  to  total
emissions of traffic entrained dust in
urban areas.
  Based on the above criteria, seven sites
were selected for this testing program:

Kansas City Area - three sites

7th Street in  Kansas City, KS (commer-
 cial/industrial)
Volker  Boulevard/Rockhill   Road  in
 Kansas City, MO (commercial/residen-
 tial)
4th Street in Tonganoxie, KS (rural town)

St. Louis, MO - two sites

I-44 (expressway)
Kingshighway (commercial/residential)

Granite City, IL - two sites

Madison Street (commercial/residential)
Benton Road  (commercial /residential)

Sampling  Equipment
  A variety of sampling equipment was
utilized in   this  study  to  measure
paniculate emissions, roadway surface
paniculate loadings, and traffic charac-
teristics.
 'The basic  emission  sampling
equipment included an isokinetic expo-
sure profiling system with four  sampling
heads positioned at 1 - to 4-m heights. In
addition,  high-volume  samplers, each
fitted with a size selective inlet (SSI) and a
parallel-slot cascade impactor (Cl), were
placed at 1-and 3-m heights to determine
the respective IP mass fractions of the
total  paniculate  emissions  and   the
corresponding particle size distributions.
The five-stage cascade impactors had, at
a flow rate of 40 scfm (1133 L/min), 50%
efficiency cutpoints at 7.2,3.0,1.5,0.95,
and 0.49 //m aerodynamic diameter. The
impactor  substrates were greased to
reduce particle bounce. A standard high-
volume air sampler was operated  at a
height of 2 m. Normally, these  sampling
devices were positioned 5 m  from  the
downwind edge of the road.
  The basic upwind equipment included
SSIs and  a  standard high-volume air
sampler. In the Kansas City testing,  two
SSIs at heights of 2 and 4 m were used to
obtain the IP concentration of upwind
paniculate matter. In the St. Louis testing,
the primary upwind equipment included a
high-volume air  sampler and an SSI/CI
with greased substrates.
  Samples of the dust on the roadway
surface were collected during the source
tests. To collect this surface dust, it was
necessary to close each traffic lane for
about 15 min. Normally, an area that was
3 m by the width of a lane was sampled.
For each test, collection of material from
all travel lanes and curb areas (extending
to about 25 to 30 cm from the curbing)
was attempted.  A hand-held  portable
vacuum cleaner  was used to collect the
roadway dust. The attached brush on the
collection   inlet  was used  to abrade
surface-compacted dust and to remove
dust from the crevices of the road surface.
Vacuuming  was  preceded  by broom
sweeping  if large aggregate was present.
  Characteristics of the vehicular traffic
during the source testing were deter-
mined both automatically and manually.
The  characteristics  included:  (a) total
traffic count, (b)  mean traffic speed, and
(c) vehicle mix.
  Total  vehicle  count was determined
using  pneumatic-tube  counters.  To
convert the axle  counts to total vehicles,
vehicle mix was determined visually over
1-min intervals every 15 min during the
source  testing.  The  vehicle  mix
summaries  recorded  vehicle  type,
number of vehicle axles, and number of
vehicle  wheels.  From this information,
the total axle counts were corrected to the
total number of vehicles by type.
  The speed of the freely flowing traffic
was taken to be the posted speed limit of
the roadway test section. As a check,
speeds of the vehicles were determined
occasionally using  a hand-held  radar
gun. The  weights of the  vehicle types
were estimated by consulting automobile
literature  and distributors of  medium-
duty and semi-trailer trucks.

Sampling and Analysis
Procedures
  The sampling and analysis procedures
used in this study were subject to quality
control  (QC) guidelines which met or
exceeded  the requirements specified by
EPA. As pan of the QC program for this
study, sampling and analysis procedures
were audited routinely, to demonstrate
that measurements  were made within
acceptable control conditions for panicu-
late source sampling and  to assess the  j
source  testing   data for precision and
accuracy. Audit  items  included  gravi-

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metric analysis, f lowrate calibration, data
processing,  and  emission  factor
calculation.
  Prior  to  equipment  deployment,  a
number of decisions were made as to the
potential for acceptable source  testing
conditions. These decisions were based
on forecast information obtained from the
local  U.S. Weather  Service  office.  A
specific sampling location was identified
based on the anticipated wind direction.
Sampling would be initiated only if the
wind  speed was forecast between 4 and
20 miles per hour (6  and 32 km/hr).
Sampling was not planned if there was a
high probability of  measurable precipita-
tion (normally >  20%) or if the road
surface was damp.
  Sampling usually  lasted 4 to 6  hr.
Occasionally sampling  was interrupted
because of unacceptable meteorological
conditions and then restarted when con-
ditions were suitable. The unacceptable
meteorological  conditions   most
frequently encountered consisted of light
winds (below 4 mph  or 6 km/hr) and in-
sufficient angle (< 45 degrees) between
mean (15-min average) wind direction
and road direction.
  The vertical distributions of exposure
(i.e., the product of plume concentration
and mean wind speed) were numerically
integrated to calculate emission factors.
The size selective inlet/cascade impactor
sampler combinations provided reliable
point concentrations for   IP   and  finer
particle size fractions. Plume height was
determined   by extrapolation of  the
vertical profile of total paniculate concen-
tration as measured by the MRI exposure
profiler.

Test Results
  Table  1  summarizes, by   land  use
category, the emission factor data and the
corresponding source characteristics. As
can  be  seen, the  smallest  emission
factors  were  measured in  the freeway
category,  which  also  had the  lowest
surface  silt   loadings. The  highest
emission  factor was measured in the
         rural town category which showed a cor-
         respondingly high surface silt loading.
         Multiple Regression Analysis
          The  source  tests  were  evaluated
         according to established QA criteria for
         exposure profiling. Seven  of  the nine
         Kansas  City tests  met all  of  the QA
         criteria, while only three of the ten tests
         conducted in the St. Louis/ Granite City
         area met the QA criteria. The spring test-
         ing  in  particular,  was hampered by
         unseasonably light winds. Wind speed for
         four of the ten spring tests did not meet
         the  minimum wind speed criterion of 4
         mph (6 km/hr).
          Stepwise  multiple  linear regression
         (MLR) was used to evaluate independent
         variables for possible use as correction
         factors in a predictive emission factor
         equation. Because it was desirable to
         have multiplicative rather than additive
         correction factors in the emission factor
         equations, all independent and depend-
         ent  variable data were transformed to
         natural logarithms before being entered
         in the MLR program.
          The independent variables evaluated
         initially  as possible  correction  factors
         were  silt loading  (g/m2), total  loading
         (g/m2), average vehicle speed  (km/hr),
         and average  vehicle weight (Mg). The
         rationale  for  including  measures of
         roadway paniculate loading stems from
         findings  of an  earlier program  that
         indicated that the magnitude of roadway
         emissions  was  directly   related  to
         variations  in  surface  loadings.  The
         vehicle  parameters—mean weight and
         speed—were included largely by analogy
         to a predictive emission factor equation
         for  unpaved roads,  although  it  was
         recognized  that the  dust  generation
         mechanism for  paved roads  may differ
         from  that   for  unpaved  roads.  The
         moisture content  of  the road  surface
         particulate  was  not  included  as  a
         correction  parameter because  of the
         difficulty of collecting a sample without
         altering  its moisture content.
                                                           The  resulting  MLR  equation  after
                                                          normalization to a typical value for silt
                                                          loading was:
 Table 1.   Mean Emission Factors and Source Characterization Parameters
Land Use Category
No. of  Emission Factor  (g/VKT)
Tests   <.15fim  <,1Q fim ^.2.5 urn
                                                        Silt
                                                      Loading
                              Vehicle  Vehicle
                               Speed  Weight
                              (km/hr)   (Mg)
Commercial/Industrial
2.43
                2.07    1.31
                       0.29
                                                                48
                                                4.1
Commercial/ Residential
Expressway
Rural Town
10
4
1
0.94
0.14
8.77
0.80
0.13
6.96
0.46
0.066
1.42
0.54
0.022
2.5
53
89
32
2.1
4.0
2.0
         *IP= 254 <-blr)0-8      (1)
where:

    elP = IP emission factor, g/VKT


sL = Silt loading of road surface par-
  ticulate matter, g/m2.

This equation  explained  73%  of  the
variation  on the emission  factors. The
MLR data set did contain data from all the
land use categories sampled during the
field program.
  The  emission  factor equation  was
found to predict the MLR series test data
with  a precision  factor  of  2.0. The
precision factor (f) for an emission factor
is defined such that the 68% confidence
interval for a predicted value (P) extends
from P/f  to Pf. The precision factor is
determined  by  exponentiating  the
standard  deviation of the  differences
(standard  error of the estimate) between
the natural logarithms of the predicted
and observed emission factors.
  The precision factor may be interpreted
as  a measure of  "average" error in
predicting  IP emissions from  the
regression equation. Assuming that the
actual  IP emission factors are normally
distributed about  the regression  line,
about 68% of the predictions are within a
factor of 2. The effective outer bounds of
predictability are determined  by expo-
nentiating twice the standard error of the
estimate.   The resultant   estimate  of
predictive accuracy, in this case 4.0, then
encompasses  about 95%  of   the
predictions.
  To put the precision factor of the IP pre-
dictive emission factor equation emission
factor into perspective, two comparisons
were undertaken  utilizing the  single-
valued emission factor found in EPA's
AP-42. However, before valid compari-
sons could be made, it was  necessary to
convert the AP-42 single-valued factor
(which represents TSP emissions) to an
approximate IP emission factor. This was
done by multiplying the AP-42 value by
0.4, which is the mean ratio  of net IP
(downwind minus upwind) to net TSP
concentrations  as  determined from the
data collected in this study.
  The first comparison involved calcula-
ting a precision factor  for the AP-42 data
set. The  resulting value  of  2.1  is  a
measure  of the  ability of  the  single-
valued factor to represent the 40 pieces

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of data which were averaged originally to
produce the AP-42 factor. The second
comparison involved calculating a preci-
sion factor using the single-valued AP-42
factor to represent the MLR data set, as
collected in this study. This comparison
yielded a precision factor of 4.4.
  The  most important conclusion that
can be drawn from these comparisons is
that the emission factor equation, though
far from ideal, does predict IP emissions
more accurately  over a  much greater
range  of values  than does the AP-42
single-valued factor over a considerably
smaller range of data values correspond-
ing to the AP-42  data set. Furthermore,
applying the single-value AP-42 factor (to
represent the wide range of IP emissions
from paved roads as measured during this
program) yields a  precision factor that is
more than double (4.4 vers'us 2.0) that
associated with the predictive equation.
This ability of the predictive equation to
more accurately represent variations in IP
emissions is directly attributable to the
relatively  strong  relationship between
roadway surface silt loading and  IP
emissions.
  Predictive emission factor equations
for  the  PM-10  and  FP  particle size
fractions were developed using the same
procedure as that applied  in developing
the equation for  IP. Derivation of TSP
emission factors for use in developing a
predictive equation  required different
initial calculations, since only two TSP
samplers (one upwind, one downwind)
were operated during  the measurement
phase  of the program. In  essence,  the
initial  calculation involved multiplying
the IP emission factor for each run in the
MLR data set by  the corresponding net
ratio  of  TSP to  IP  concentration as
measured by appropriate samplers. This
procedure assumed that the TSP/IP ratio
was constant over the vertical extent of
the plume.
  The general form of the emission factor
equations,  applicable to the additional
particle size fractions, was the same as
Equation 1:
      Table 2.   Paved  Road  Emission  Factor
                Equation Parameters (by particle
                size fraction)
            e =
                    sL
                    0.5
(2)
The base emission factor coefficient (k),
exponent (P), and  precision factor for
each size fraction are listed in Table 2.
  Note that the tendency for the power
term in  the  equation to increase witn
larger particle size  fraction is generally
consistent with the previous paved road
equation in which silt loading to the 1.0
power was  employed  to  account for
variations in  TSP emissions.
Particle Size
Fraction
TSP
IP
10 fjm
FP
k (g/VKT)
5.87
2.54
2.28
1.02
P
0.9
O.8
0.8
0.6
Precision
Factor3
2.4
2.0
2.2
2.2
      Represents the interval encompassing 68%
       of the predicted values.

      Emissions Inventory
      Applications
        For  most emissions  inventory
      applications involving urban paved roads,
      silt loading will probably not actually be
      measured. Therefore, to facilitate the use
      of the previously described equations, it
      was  necessary  to  characterize  silt
      loadings  according  to  a  parameter(s)
      more readily available to developers of
      emissions inventories. After examination
      and analysis of silt loading and traffic data
      collected during relevant MRI sampling
      programs, as well as surface loading data
      gathered in connection with an extensive
      study of  urban water   pollution, the
      decision  was   made  to characterize
      variations in silt loading based on the
      roadway classification system shown in
      Table  3.  This  system  generally
      corresponds  to  the  functional
      classification systems   employed  by
      transportation  agency  personnel;  and
      thus the data necessary for emissions
       inventory—number of road miles per road
       category and traffic counts— should be
       easily obtainable.

       Table 3.   Paved Roadway Classification
           Roadway Type
 Average
   Daily
   Traffic    No. of
   (ADT)     Lanes
       Freeways/Expressways
       Major streets/highways
       Collector streets
       Local streets
  > 50,000 >4
  > 10,000 > 4
500-10,000   2"
     <500   2b
       ' Total roadway width 2 32 ft (9.75 m).
       b Total roadway width < 32 ft (9.75 m).

       The data base made up of 44 samples col-
       lected and analyzed according to the
       procedures outlined above, may be used
       to characterize the silt loadings for each
       roadway   category.  These  samples,
       obtained during field sampling programs
       over the past 3 years, represent a  broad
       range of urban land use  and roadway
       conditions. Geometric  means for this
 data  set  are broken  out  by sampling
 location (i.e., city) and roadway category
 in Table 4.
  Table 5 presents the emission factors
 broken out  by  roadway category and
 particle size. These  were obtained by
 inserting  the above mean silt loadings
 into the emission factor equations with
 parameters defined in Table 2.  These
 emission  factors can  be  utilized directly
 for emission inventory  purposes. It is
 important to note that the current AP-42
 paved road  emission factors for TSP
 agree quite well with  those developed in
 this study.  For example, those cited in
 connection  with previous testing were
 conducted at two roadway  sites in  the
 major  street and highway category.
 Those tests yielded a mean TSP emission
 factor of 4.3 g/VKT versus 4.4 g/VKT as
 determined from the data presented here.

Summary and Conclusions
  The purpose of this study was to quan-
tify size-specific  particulate  emissions
generated by traffic entrainment of paved
road  surface particulate matter.  Paved
road  source testing was performed at
sites  representing significant emission
sources within a broad range of  urban
land-use categories.
  The measured  inhalable  particulate
emission factors spanned two orders of
magnitude (0.06  to 8.8 g/VKT). Lowest
mean emissions were measured for the
expressway  category;   highest  mean
emissions were  measured for*the rural
town  category.  About 90% of the IP
emissions consisted of particles £ 10/um
 in aerodynamic diameter, and about 50%
of the IP emission consisted of particles •£
2.5 fjm in aerodynamic diameter.
  Correlation analysis of IP emissions
with   parameters  characterizing  the
source conditions showed the existence
of a relatively strong positive relationship
 between  intensity of emissions  and
 roadway  surface  silt  loading.  This
 confirms the findings of earlier testing.
 Regression   analysis   of  a  subset of
 acceptable (MLR) test runs was used to
 derive a  predictive IP emission  factor
 equation  which  explained 73%  of  the
 variation in the emission factors.
  This predictive equation has an associ-
 ated  precision factor of 2.0 in relation to
 the MLR data set. By way of comparison,
 the AP-42 single-value factor (corrected
 to represent IP emissions) has a precision
 factor of 2.1 for its data set and a preci-
 sion  factor  of 4.4 for the MLR data set,
 which  spans a  much larger range of
 values than the  AP-42 data set.  There-
 fore, the predictive equation, though far

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Table 4.   Summary of Sift Loadings for Urban Paved Roadways (g/m2) '
                                            Roadway Category
Local
Cityi
Baltimore
Buffalo
jranite City (IL)
Kansas City
St. Louis
Overall
1
1.
1.
-
1.

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Environmental Protection
Agency
Center for Environmental Research
Information
Cincinnati OH 45268
Official Business
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