9/
98
Miscellaneous
Sources
13.2.2­
1
13.2.2
Unpaved
Roads
13.2.2.1
General
When
a
vehicle
travels
an
unpaved
road,
the
force
of
the
wheels
on
the
road
surface
causes
pulverization
of
surface
material.
Particles
are
lifted
and
dropped
from
the
rolling
wheels,
and
the
road
surface
is
exposed
to
strong
air
currents
in
turbulent
shear
with
the
surface.
The
turbulent
wake
behind
the
vehicle
continues
to
act
on
the
road
surface
after
the
vehicle
has
passed.

13.2.2.2
Emissions
Calculation
And
Correction
Parameters1­
6
The
quantity
of
dust
emissions
from
a
given
segment
of
unpaved
road
varies
linearly
with
the
volume
of
traffic.
Field
investigations
also
have
shown
that
emissions
depend
on
source
parameters
that
characterize
the
condition
of
a
particular
road
and
the
associated
vehicle
traffic.
Characterization
of
these
source
parameters
allow
for
"
correction"
of
emission
estimates
to
specific
road
and
traffic
conditions.

Dust
emissions
from
unpaved
roads
have
been
found
to
vary
directly
with
the
fraction
of
silt
(
particles
smaller
than
75
micrometers
[
µ
m]
in
diameter)
in
the
road
surface
materials.
1
The
silt
fraction
is
determined
by
measuring
the
proportion
of
loose
dry
surface
dust
that
passes
a
200­
mesh
screen,
using
the
ASTM­
C­
136
method.
A
summary
of
this
method
is
contained
in
Appendix
C
of
AP­
42.
Table
13.2.2­
1
summarizes
measured
silt
values
for
industrial
and
public
unpaved
roads.
It
should
be
noted
that
the
ranges
of
silt
content
vary
over
two
orders
of
magnitude.
Therefore,
the
use
of
data
from
this
table
can
potentially
introduce
considerable
error.
Use
of
this
data
is
strongly
discouraged
when
it
is
feasible
to
obtain
locally
gathered
data.

Since
the
silt
content
of
a
rural
dirt
road
will
vary
with
geographic
location,
it
should
be
measured
for
use
in
projecting
emissions.
As
a
conservative
approximation,
the
silt
content
of
the
parent
soil
in
the
area
can
be
used.
Tests,
however,
show
that
road
silt
content
is
normally
lower
than
in
the
surrounding
parent
soil,
because
the
fines
are
continually
removed
by
the
vehicle
traffic,
leaving
a
higher
percentage
of
coarse
particles.

The
PM­
10
and
TSP
emission
factors
presented
below
are
the
outcomes
from
stepwise
linear
regressions
of
field
emission
test
results
of
vehicles
traveling
over
unpaved
surfaces.
The
results
from
180
PM­
10
and
92
TSP
field
tests
were
used
to
develop
the
predictive
emission
factor
expressions.
Due
to
a
limited
amount
of
information
available
for
PM­
2.5,
the
expression
for
that
size
range
has
been
scaled
against
the
result
for
PM­
10.
Consequently,
the
quality
rating
for
the
PM­
2.5
factor
is
lower
than
that
for
the
PM­
10
expression.
The
background
document
for
AP­
42
Section
13.2.2
(
Reference
6)
fully
describes
the
process
used
to
develop
and
validate
the
emission
factor
expressions.
13.2.2­
2
EMISSION
FACTORS
9/
98
Table
13.2.2­
1.
TYPICAL
SILT
CONTENT
VALUES
OF
SURFACE
MATERIAL
ON
INDUSTRIAL
AND
RURAL
UNPAVED
ROADSa
Industry
Road
Use
Or
Surface
Material
Plant
Sites
No.
Of
Samples
Silt
Content
(%)

Range
Mean
Copper
smelting
Plant
road
1
3
16
­
19
17
Iron
and
steel
production
Plant
road
19
135
0.2
­
19
6.0
Sand
and
gravel
processing
Plant
road
1
3
4.1
­
6.0
4.8
Material
storage
area
1
1
­
7.1
Stone
quarrying
and
processing
Plant
road
2
10
2.4
­
16
10
Haul
road
to/
from
pit
4
20
5.0­
15
8.3
Taconite
mining
and
processing
Service
road
1
8
2.4
­
7.1
4.3
Haul
road
to/
from
pit
1
12
3.9
­
9.7
5.8
Western
surface
coal
mining
Haul
road
to/
from
pit
3
21
2.8
­
18
8.4
Plant
road
2
2
4.9
­
5.3
5.1
Scraper
route
3
10
7.2
­
25
17
Haul
road
(
freshly
graded)
2
5
18
­
29
24
Construction
sites
Scraper
routes
7
20
0.56­
23
8.5
Lumber
sawmills
Log
yards
2
2
4.8­
12
8.4
Municipal
solid
waste
landfills
Disposal
routes
4
20
2.2
­
21
6.4
Publicly
accessible
roads
Gravel/
crushed
limestone
9
46
0.1­
15
6.4
Dirt
(
i.
e.,
local
material
compacted,
bladed,
and
crowned)
8
24
0.83­
68
11
aReferences
1,5­
16.
9/
98
Miscellaneous
Sources
13.2.2­
3
E
'
k
(
s/
12)
a
(
W/
3)
b
(
M/
0.2)
c
(
1)
The
following
empirical
expression
may
be
used
to
estimate
the
quantity
in
pounds
(
lb)
of
size­
specific
particulate
emissions
from
an
unpaved
road,
per
vehicle
mile
traveled
(
VMT):

where
k,
a,
b
and
c
are
empirical
constants
(
Reference
6)
given
below
and
E
=
size­
specific
emission
factor
(
lb/
VMT)
s
=
surface
material
silt
content
(%)
W
=
mean
vehicle
weight
(
tons)
M
=
surface
material
moisture
content
(%)

The
source
characteristics
s,
W
and
M
are
referred
to
as
correction
parameters
for
adjusting
the
emission
estimates
to
local
conditions.
The
metric
conversion
from
lb/
VMT
to
grams
(
g)
per
vehicle
kilometer
traveled
(
VKT)
is
as
follows:

1
lb/
VMT
=
281.9
g/
VKT
The
constants
for
Equation
1
based
on
the
stated
aerodynamic
particle
sizes
are
shown
in
Table
13.2.2­
2.

Table
13.2.2­
2.
CONSTANTS
FOR
EQUATION
1
Constant
PM­
2.5
PM­
10
PM­
30a
k
(
lb/
VMT)
0.38
2.6
10
a
0.8
0.8
0.8
b
0.4
0.4
0.5
c
0.3
0.3
0.4
Quality
rating
C
B
B
aAssumed
equivalent
to
total
suspended
particulate
(
TSP).

Table
13.2.2­
2
also
contains
the
quality
ratings
for
the
various
size­
specific
versions
of
Equation
1.
The
equation
retains
the
assigned
quality
rating,
if
applied
within
the
ranges
of
source
conditions,
shown
in
Table
13.2.2­
3,
that
were
tested
in
developing
the
equation:

Table
13.2.2­
3.
RANGE
OF
SOURCE
CONDITIONS
USED
IN
DEVELOPING
EQUATION
1
Surface
Silt
Content,
%
Mean
Vehicle
Weight
Mean
Vehicle
Speed
Mean
No.
of
Wheels
Surface
Moisture
Content,
%
Mg
ton
km/
hr
mph
1.2­
35
1.4­
260
1.5­
290
8­
88a
5­
55a
4­
7a
0.03­
20
a
See
discussion
in
text.

As
noted
earlier,
Equation
1
was
developed
from
tests
of
traffic
on
unpaved
surfaces,
either
uncontrolled
or
watered.
Unpaved
roads
have
a
hard,
generally
nonporous
surface
that
usually
dries
quickly
after
a
rainfall
or
watering,
because
of
traffic­
enhanced
natural
evaporation.
(
Factors
influencing
13.2.2­
4
EMISSION
FACTORS
9/
98
how
fast
a
road
dries
are
discussed
in
Section
13.2.2.3,
below.)
The
quality
ratings
given
above
pertain
to
the
mid­
range
of
the
measured
source
conditions
for
the
equation.
A
higher
mean
vehicle
weight
and
a
higher
than
normal
traffic
rate
may
be
justified
when
performing
a
worst­
case
analysis
of
emissions
from
unpaved
roads.

It
is
important
to
note
that
the
vehicle­
related
source
conditions
refer
to
the
average
weight,
speed,
and
number
of
wheels
for
all
vehicles
traveling
the
road.
For
example,
if
98
percent
of
traffic
on
the
road
are
2­
ton
cars
and
trucks
while
the
remaining
2
percent
consists
of
20­
ton
trucks,
then
the
mean
weight
is
2.4
tons.
More
specifically,
Equation
1
is
not
intended
to
be
used
to
calculate
a
separate
emission
factor
for
each
vehicle
class
within
a
mix
of
traffic
on
a
given
unpaved
road.
That
is,
in
the
example,
one
should
not
determine
one
factor
for
the
2­
ton
vehicles
and
a
second
factor
for
the
20­
ton
trucks.
Instead,
only
one
emission
factor
should
be
calculated
that
represents
the
"
fleet"
average
of
2.4
tons
for
all
vehicles
traveling
the
road.

Furthermore,
although
mean
vehicle
speed
and
the
mean
number
of
wheels
do
not
explicitly
appear
in
the
predictive
equation,
these
variables
should
be
considered
when
determining
quality
ratings.
During
the
validation
of
Equation
1,
it
was
found
that
the
predictive
equation
tends
to
overpredict
emissions
for
very
slow
mean
vehicle
speeds.

The
background
document
(
Reference
6)
discusses
this
tendency
for
very
slow
vehicles
speeds.
The
background
document
further
notes
that
no
bias
is
evident
for
mean
vehicle
speeds
of
at
least
15
mph.

In
the
case
of
a
mean
vehicle
speed
less
than
15
mph,
Equation
1
could
be
used
to
conservatively
estimate
the
amount
of
emissions
due
to
traffic
over
the
unpaved
surface.
Should
one
wish
to
account
for
the
tendency
for
Equation
1
to
overestimate
at
low
speeds,
it
is
recommended
that
Equation
1
be
multiplied
by
(
S/
15),
where
S
is
the
average
vehicle
speed
(
mph)
and
S
#
15
mph.
Again,
note
that
this
applies
only
to
situations
in
which
the
average
vehicle
speed
is
less
than
15
mph.
Furthermore,
if
Equation
1
is
multiplied
by
(
S/
15),
then
the
quality
rating
of
the
emission
estimate
should
be
downgraded
by
at
least
one
letter.

Moreover,
to
retain
the
quality
ratings
when
addressing
a
group
of
unpaved
roads,
it
is
necessary
that
reliable
correction
parameter
values
be
determined
for
the
road
in
question.
The
field
and
laboratory
procedures
for
determining
road
surface
silt
and
moisture
contents
are
given
in
AP­
42
Appendices
C.
1
and
C.
2.
Vehicle­
related
parameters
should
be
developed
by
recording
visual
observations
of
traffic.
In
some
cases,
vehicle
parameters
for
industrial
unpaved
roads
can
be
determined
by
reviewing
maintenance
records
or
other
information
sources
at
the
facility.

In
the
event
that
site­
specific
values
for
correction
parameters
cannot
be
obtained,
then
default
values
may
be
used.
A
default
value
of
2.2
tons
is
recommended
for
the
mean
vehicle
weight
on
publicly
accessible
unpaved
roads.
(
It
is
assumed
that
readers
addressing
industrial
roads
have
access
to
the
information
needed
to
develop
average
vehicle
information
for
their
facility.)
In
the
absence
of
site­
specific
silt
content
information,
an
appropriate
mean
value
from
Table
13.2.2­
1
may
be
used
as
a
default
value,
but
the
quality
rating
of
the
equation
is
reduced
by
two
letters.
Because
of
significant
differences
found
between
different
types
of
road
surfaces
and
between
different
areas
of
the
country,
use
of
the
default
moisture
content
value
of
0.2
percent
for
dry
conditions
is
discouraged.
The
quality
rating
should
be
downgraded
two
letters
when
the
default
moisture
content
value
is
used.

The
effect
of
routine
watering
to
control
emissions
from
unpaved
roads
is
discussed
below
in
Section
13.2.2.3,
"
Controls".
However,
all
roads
are
subject
to
some
natural
mitigation
because
of
rainfall
and
other
precipitation.
Equation
1
can
be
extrapolated
to
annual
average
uncontrolled
conditions
(
but
9/
98
Miscellaneous
Sources
13.2.2­
5
E
ext
'
k
(
s/
12)
a
(
W/
3)
b)

(
M
dry/
0.2)
c
[(
365
&
p)
/
365]
(
2)
including
natural
mitigation)
under
the
simplifying
assumption
that
annual
average
emissions
are
inversely
proportional
to
the
number
of
days
with
measurable
(
more
than
0.254
mm
[
0.01
inch])
precipitation:

where
s,
W,
k,
a,
b
and
c
are
as
given
earlier
and
Eext
=
annual
size­
specific
emission
factor
extrapolated
for
natural
mitigation,
lb/
VMT
Mdry
=
surface
material
moisture
content
under
dry,
uncontrolled
conditions,
%
p
=
number
of
days
with
at
least
0.254
mm
(
0.01
in)
of
precipitation
per
year
(
see
below)

Figure
13.2.2­
1
gives
the
geographical
distribution
for
the
mean
annual
number
of
"
wet"
days
for
the
United
States.
Although
the
use
of
information
from
this
table
is
reasonable
for
estimating
an
average
emission
factor,
it
would
not
be
reasonable
to
use
this
information
to
estimate
an
actual
emission
factor
for
a
specific
year.
Reported
meteorological
information
should
be
used
for
estimating
actual
emission
factors.

It
is
emphasized
that
the
moisture
content
to
be
used
in
Equation
2
­­
Mdry
­­
must
reference
dry,
worst­
case
conditions.
In
the
absence
of
the
appropriate
site­
specific
information,
the
default
value
of
0.2
percent
should
be
used
in
Equation
2.

Equation
2
provides
an
estimate
that
accounts
for
precipitation
on
an
annual
average
basis
for
the
purpose
of
inventorying
emissions.
It
should
be
noted
that
Equation
2
does
not
account
for
differences
in
the
temporal
distributions
of
the
rain
events,
the
quantity
of
rain
during
any
event,
or
the
potential
for
the
rain
to
evaporate
from
the
road
surface.
In
the
event
that
a
finer
temporal
and
spatial
resolution
is
desired
for
inventories
of
public
unpaved
roads,
estimates
can
be
based
on
a
more
complex
set
of
assumptions.
These
assumptions
include:

1.
The
moisture
content
of
the
road
surface
material
is
increased
in
proportion
to
the
quantity
of
water
added;
2.
The
moisture
content
of
the
road
surface
material
is
reduced
in
proportion
to
the
Class
A
pan
evaporation
rate;
3.
The
moisture
content
of
the
road
surface
material
is
reduced
in
proportion
to
the
traffic
volume;
and
4.
The
moisture
content
of
the
road
surface
material
varies
between
the
extremes
observed
in
the
area.
The
CHIEF
Web
site
(
http://
www.
epa.
gov/
ttn/
chief/
ap42back.
html)
has
a
file
which
contains
a
spreadsheet
program
for
calculating
emission
factors
which
are
temporally
and
spatially
resolved.
Information
required
for
use
of
the
spreadsheet
program
includes
monthly
Class
A
pan
evaporation
values,
hourly
meteorological
data
for
precipitation,
humidity
and
snow
cover,
vehicle
traffic
information,
and
road
surface
material
information.

It
is
emphasized
that
the
simple
assumption
underlying
Equation
2
and
the
more
complex
set
of
assumptions
underlying
the
use
of
the
procedure
which
produces
a
finer
temporal
and
spatial
resolution
have
not
been
verified
in
any
rigorous
manner.
For
this
reason,
the
quality
ratings
for
either
approach
should
be
downgraded
one
letter
from
the
rating
that
would
be
applied
to
Equation
1.
13.2.2­
6
EMISSION
FACTORS
9/
98
13.2.2.3
Controls18­
22
A
wide
variety
of
options
exist
to
control
emissions
from
unpaved
roads.
Options
fall
into
the
following
three
groupings:

1.
Vehicle
restrictions
that
limit
the
speed,
weight
or
number
of
vehicles
on
the
road;
2.
Surface
improvement,
by
measures
such
as
(
a)
paving
or
(
b)
adding
gravel
or
slag
to
a
dirt
road;
and
3.
Surface
treatment,
such
as
watering
or
treatment
with
chemical
dust
suppressants.

Available
control
options
span
broad
ranges
in
terms
of
cost,
efficiency,
and
applicability.
For
example,
traffic
controls
provide
moderate
emission
reductions
(
often
at
little
cost)
but
are
difficult
to
enforce.
Although
paving
is
highly
effective,
its
high
initial
cost
is
often
prohibitive.
Furthermore,
paving
is
not
feasible
for
industrial
roads
subject
to
very
heavy
vehicles
and/
or
spillage
of
material
in
transport.
Watering
and
chemical
suppressants,
on
the
other
hand,
are
potentially
applicable
to
most
industrial
roads
at
moderate
to
low
costs.
However,
these
require
frequent
reapplication
to
maintain
an
acceptable
level
of
control.
Chemical
suppressants
are
generally
more
cost­
effective
than
water
but
not
in
cases
of
temporary
roads
(
which
are
common
at
mines,
landfills,
and
construction
sites).
In
summary,
then,
one
needs
to
consider
not
only
the
type
and
volume
of
traffic
on
the
road
but
also
how
long
the
road
will
be
in
service
when
developing
control
plans.

Vehicle
restrictions.
These
measures
seek
to
limit
the
amount
and
type
of
traffic
present
on
the
road
or
to
lower
the
mean
vehicle
speed.
For
example,
many
industrial
plants
have
restricted
employees
from
driving
on
plant
property
and
have
instead
instituted
bussing
programs.
This
eliminates
emissions
due
to
employees
traveling
to/
from
their
worksites.
Although
the
heavier
average
vehicle
weight
of
the
busses
increases
the
base
emission
factor,
the
decrease
in
vehicle­
miles­
traveled
results
in
a
lower
overall
emission
rate.

Although
vehicle
speed
does
not
appear
as
a
correction
parameter,
it
is
obvious
to
anyone
who
has
driven
on
an
unpaved
road
that
(
visible)
emissions
increase
with
vehicle
speed.
Accordingly,
speed
reduction
is
a
clearly
viable
control
measure.
However,
as
with
the
source
parameters
that
do
appear
in
Equation
1,
the
control
measure
must
effectively
reduce
the
fleet
average
speed.
In
order
to
substantially
reduce
the
speed
of
all
vehicles,
this
control
option
is
most
applicable
to
rural
public
roads.
However,
effective
enforcement
of
the
new
speed
limit
may
prove
problematic.

Currently
available
short­
term
tests
suggest
that
the
control
efficiency
afforded
by
speed
reduction
should
be
considered
as
linear.
Thus,
if
the
average
speed
is
effectively
reduced
by
30
percent
(
e.
g.,
from
50
to
35
mph),
then
a
control
efficiency
of
30
percent
should
be
applied
to
the
emission
factor.
The
background
document
discusses
how
past
testing
programs
used
"
captive"
traffic
to
tightly
control
vehicular
characteristics.
These
tests
involve
very
short
periods
(
1
to
2
hr)
of
increased
or
reduced
travel
speeds.
Under
these
conditions,
it
was
found
that
emissions
depend
upon
speed
raised
to
a
power
between
1
and
2.
However,
exploratory
analysis
of
the
data
supporting
the
equation
in
this
section
indicated
that
emissions
were
poorly
correlated
with
speed
raised
to
the
power
of
approximately
0.3.
As
a
result,
it
is
believed
that
if
the
long­
term,
average
speed
is
reduced
on
an
unpaved
road,
the
road
surface
silt
content
can
be
expected
to
change.
In
other
words,
the
silt
content
will
reach
a
new
equilibrium
condition
as
the
grinding
of
material
is
balanced
by
the
emission
process.
It
is
strongly
recommended
that
any
prospective
emission
reduction
credit
based
upon
speed
reduction
be
based
upon
the
ratio
of
speeds
raised
to
the
0.3
power.
After
6
months
operation
at
the
slower
speed
a
new
road
surface
sample
should
be
collected
9/
98
Miscellaneous
Sources
13.2.2­
7
Figure
13.2.2­
1.
Mean
number
of
days
with
0.01
inch
or
more
of
precipitation
in
United
States.
13.2.2­
8
EMISSION
FACTORS
9/
98
and
analyzed
(
in
the
manner
described
in
Appendices
C.
1
and
C.
2).
The
new
surface
silt
content
should
then
be
used
in
Equation
1
for
calculation
of
a
new
uncontrolled
emission
factor,
without
further
adjustment
for
speed.

Surface
improvements.
Control
options
in
this
category
alter
the
road
surface.
As
opposed
to
the
"
surface
treatments"
discussed
below,
improvements
are
relatively
"
permanent"
and
do
not
require
periodic
retreatment.

The
most
obvious
surface
improvement
is
paving
an
unpaved
road.
This
option
is
quite
expensive
and
is
probably
most
applicable
to
relatively
short
stretches
of
unpaved
road
with
at
least
several
hundred
vehicle
passes
per
day.
Furthermore,
if
the
newly
paved
road
is
located
near
unpaved
areas
or
is
used
to
transport
material,
it
is
essential
that
the
control
plan
address
routine
cleaning
of
the
newly
paved
road
surface.

The
control
efficiencies
achievable
by
paving
can
be
estimated
by
comparing
emission
factors
for
unpaved
and
paved
road
conditions.
The
predictive
emission
factor
equation
for
paved
roads,
given
in
Section
13.2.1,
requires
estimation
of
the
silt
loading
on
the
traveled
portion
of
the
paved
surface,
which
in
turn
depends
on
whether
the
pavement
is
periodically
cleaned.
Unless
curbing
is
to
be
installed,
the
effects
of
vehicle
excursion
onto
unpaved
shoulders
(
berms)
also
must
be
taken
into
account
in
estimating
the
control
efficiency
of
paving.

Other
improvement
methods
cover
the
road
surface
with
another
material
that
has
a
lower
silt
content.
Examples
include
placing
gravel
or
slag
on
a
dirt
road.
Control
efficiency
can
be
estimated
by
comparing
the
emission
factors
obtained
using
the
silt
contents
before
and
after
improvement.
The
silt
content
of
the
road
surface
should
be
determined
after
3
to
6
months
rather
than
immediately
following
placement.
Control
plans
should
address
regular
maintenance
practices,
such
as
grading,
to
retain
larger
aggregate
on
the
traveled
portion
of
the
road.

Surface
treatments
refer
to
control
options
which
require
periodic
reapplication.
Treatments
fall
into
the
two
main
categories
of
(
a)
"
wet
suppression"
(
i.
e.,
watering,
possibly
with
surfactants
or
other
additives),
which
keeps
the
road
surface
wet
to
control
emissions
and
(
b)
"
chemical
stabilization/
treatment",
which
attempts
to
change
the
physical
characteristics
of
the
surface.
The
necessary
reapplication
frequency
varies
from
several
minutes
for
plain
water
under
summertime
conditions
to
several
weeks
or
months
for
chemical
dust
suppressants.

Watering
increases
the
moisture
content,
which
conglomerates
particles
and
reduces
their
likelihood
to
become
suspended
when
vehicles
pass
over
the
surface.
The
control
efficiency
depends
on
how
fast
the
road
dries
after
water
is
added.
This
in
turn
depends
on
(
a)
the
amount
(
per
unit
road
surface
area)
of
water
added
during
each
application;
(
b)
the
period
of
time
between
applications;
(
c)
the
weight,
speed
and
number
of
vehicles
traveling
over
the
watered
road
during
the
period
between
applications;
and
(
d)
meteorological
conditions
(
temperature,
wind
speed,
cloud
cover,
etc.)
that
affect
evaporation
during
the
period.

Given
the
complicated
nature
of
how
the
road
dries,
characterization
of
emissions
from
watered
roadways
is
best
done
by
collecting
material
samples
at
various
times
between
water
truck
passes.
(
Appendices
C.
1
and
C.
2
present
the
sampling
and
analysis
procedures.)
The
time­
averaged
moisture
content
is
then
substituted
into
Equation
1.
Samples
that
reflect
average
conditions
during
the
watering
cycle
can
take
the
form
of
either
a
series
of
samples
between
water
applications
or
a
single
sample
at
the
midpoint.
It
is
essential
that
samples
be
collected
during
periods
with
active
traffic
on
the
road.
Finally,
9/
98
Miscellaneous
Sources
13.2.2­
9
because
of
different
evaporation
rates,
it
is
recommended
that
samples
be
collected
at
various
times
during
the
year.
If
only
one
set
of
samples
is
to
be
collected,
these
must
be
collected
during
hot,
summertime
conditions.

When
developing
watering
control
plans
for
roads
that
do
not
yet
exist,
it
is
strongly
recommended
that
the
moisture
cycle
be
established
by
sampling
similar
roads
in
the
same
geographic
area.
If
the
moisture
cycle
cannot
be
established
by
similar
roads
using
established
watering
control
plans,
the
more
complex
methodology
used
to
estimate
the
mitigation
of
rainfall
and
other
precipitation
can
be
used
to
estimate
the
control
provided
by
routine
watering.
An
estimate
of
the
maximum
daytime
Class
A
pan
evaporation
(
based
upon
daily
evaporation
data
published
in
the
monthly
Climatological
Data
for
the
state
by
the
National
Climatic
Data
Center)
should
be
used
to
insure
that
adequate
watering
capability
is
available
during
periods
of
highest
evaporation.
The
hourly
precipitation
values
in
the
spreadsheet
should
be
replaced
with
the
equivalent
inches
of
precipitation
(
where
the
equivalent
of
1
inch
of
precipitation
is
provided
by
an
application
of
5.6
gallons
of
water
per
square
yard
of
road).
Information
on
the
long
term
average
annual
evaporation
and
on
the
percentage
that
occurs
between
May
and
October
was
published
in
the
Climatic
Atlas
(
Reference
16).
Figure
13.2.2­
2
presents
the
geographical
distribution
for
"
Class
A
pan
evaporation"
throughout
the
United
States.
Figure
13.2.2­
3
presents
the
geographical
distribution
of
the
percentage
of
this
evaporation
that
occurs
between
May
and
October.
The
U.
S.
Weather
Bureau
Class
A
evaporation
pan
is
a
cylindrical
metal
container
with
a
depth
of
10
inches
and
a
diameter
of
48
inches.
Periodic
measurements
are
made
of
the
changes
of
the
water
level.

The
above
methodology
should
be
used
only
for
prospective
analyses
and
for
designing
watering
programs
for
existing
roadways.
The
quality
rating
of
an
emission
factor
for
a
watered
road
that
is
based
on
this
methodology
should
be
downgraded
two
letters.
Periodic
road
surface
samples
should
be
collected
and
analyzed
to
verify
the
efficiency
of
the
watering
program.

As
opposed
to
watering,
chemical
dust
suppressants
have
much
less
frequent
reapplication
requirements.
These
materials
suppress
emissions
by
changing
the
physical
characteristics
of
the
existing
road
surface
material.
Many
chemical
unpaved
road
dust
suppressants
form
a
hardened
surface
that
binds
particles
together.
After
several
applications,
a
treated
road
often
resembles
a
paved
road
except
that
the
surface
is
not
uniformly
flat.
Because
the
improved
surface
results
in
more
grinding
of
small
particles,
the
silt
content
of
loose
material
on
a
highly
controlled
surface
may
be
substantially
higher
than
when
the
surface
was
uncontrolled.
For
this
reason,
Equation
1
cannot
be
used
to
estimate
emissions
from
chemically
stabilized
roads.
Should
the
road
be
allowed
to
return
to
an
uncontrolled
state
with
no
visible
signs
of
large­
scale
cementing
of
material,
Equation
1
could
then
be
used
to
obtain
conservatively
high
emission
estimates.

The
control
effectiveness
of
chemical
dust
suppressants
appears
to
depend
on
(
a)
the
dilution
rate
used
in
the
mixture;
(
b)
the
application
rate
(
volume
of
solution
per
unit
road
surface
area);
(
c)
the
time
between
applications;
(
d)
the
size,
speed
and
amount
of
traffic
during
the
period
between
applications;
and
(
e)
meteorological
conditions
(
rainfall,
freeze/
thaw
cycles,
etc.)
during
the
period.
Other
factors
that
affect
the
performance
of
dust
suppressants
include
other
traffic
characteristics
(
e.
g.,
cornering,
track­
on
from
unpaved
areas)
and
road
characteristics
(
e.
g.,
bearing
strength,
grade).
The
variabilities
in
the
above
factors
and
differences
between
individual
dust
control
products
make
the
control
efficiencies
of
chemical
dust
suppressants
difficult
to
estimate.
Past
field
testing
of
emissions
from
controlled
unpaved
roads
has
shown
that
chemical
dust
suppressants
provide
a
PM­
10
control
efficiency
of
about
80
percent
when
applied
at
regular
intervals
of
2
weeks
to
1
month.
13.2.2­
10
EMISSION
FACTORS
9/
98
Figure
13.2.2­
2.
Annual
evaporation
data.
9/
98
Miscellaneous
Sources
13.2.2­
11
Figure
13.2.2­
3.
Geographical
distribution
of
the
percentage
of
evaporation
occurring
between
May
and
October.
13.2.2­
12
EMISSION
FACTORS
9/
98
Table
13.2­
2­
4.
EXAMPLE
OF
AVERAGE
CONTROLLED
EMISSION
FACTORS
FOR
SPECIFIC
CONDITIONS
Period
Ground
Inventory,
gal/
yd2
Average
Control
Efficiency,
%
a
Average
Controlled
Emission
Factor,
lb/
VMT
May
0.037
0
7.1
June
0.073
62
2.7
July
0.11
68
2.3
August
0.15
74
1.8
September
0.18
80
1.4
a
From
Figure
13.2.2­
4,
#
10
µ
m.
Zero
efficiency
assigned
if
ground
inventory
is
less
than
0.05
gal/
yd2.
1
lb/
VMT
=
281.9
g/
VKT.
1
gal/
yd2
=
4.531
L/
m2.
Petroleum
resin
products
historically
have
been
the
dust
suppressants
(
besides
water)
most
widely
used
on
industrial
unpaved
roads.
Figure
13.2.2­
4
presents
a
method
to
estimate
average
control
efficiencies
associated
with
petroleum
resins
applied
to
unpaved
roads.
20
Several
items
should
be
noted:

1.
The
term
"
ground
inventory"
represents
the
total
volume
(
per
unit
area)
of
petroleum
resin
concentrate
(
not
solution)
applied
since
the
start
of
the
dust
control
season.

2.
Because
petroleum
resin
products
must
be
periodically
reapplied
to
unpaved
roads,
the
use
of
a
time­
averaged
control
efficiency
value
is
appropriate.
Figure
13.2.2­
4
presents
control
efficiency
values
averaged
over
two
common
application
intervals,
2
weeks
and
1
month.
Other
application
intervals
will
require
interpolation.

3.
Note
that
zero
efficiency
is
assigned
until
the
ground
inventory
reaches
0.05
gallon
per
square
yard
(
gal/
yd2).
Requiring
a
minimum
ground
inventory
ensures
that
one
must
apply
a
reasonable
amount
of
chemical
dust
suppressant
to
a
road
before
claiming
credit
for
emission
control.
Recall
that
the
ground
inventory
refers
to
the
amount
of
petroleum
resin
concentrate
rather
than
the
total
solution.

As
an
example
of
the
application
of
Figure
13.2.2­
4,
suppose
that
the
equation
was
used
to
estimate
an
emission
factor
of
7.1
lb/
VMT
for
PM­
10
from
a
particular
road.
Also,
suppose
that,
starting
on
May
1,
the
road
is
treated
with
0.221
gal/
yd2
of
a
solution
(
1
part
petroleum
resin
to
5
parts
water)
on
the
first
of
each
month
through
September.
Then,
the
average
controlled
emission
factors,
shown
in
Table
13.2.2­
4,
are
found.

Besides
petroleum
resins,
other
newer
dust
suppressants
have
also
been
successful
in
controlling
emissions
from
unpaved
roads.
Specific
test
results
for
those
chemicals,
as
well
as
for
petroleum
resins
and
watering,
are
provided
in
References
18
through
21.
9/
98
Miscellaneous
Sources
13.2.2­
13
Figure
13.2.2­
4.
Average
control
efficiencies
over
common
application
intervals.
13.2.2­
14
EMISSION
FACTORS
9/
98
13.2.2.4
Updates
Since
The
Fifth
Edition
The
Fifth
Edition
was
released
in
January
1995.
Revisions
to
this
section
since
that
date
are
summarized
below.
For
further
detail,
consult
the
background
report
for
this
section
(
Reference
6).

October
1998
(
Supplement
E)­­
This
was
a
major
revision
of
this
section.
Significant
changes
to
the
text
and
the
emission
factor
equations
were
made.

References
For
Section
13.2.2
1.
C.
Cowherd,
Jr.,
et
al.,
Development
Of
Emission
Factors
For
Fugitive
Dust
Sources,
EPA­
450/
3­
74­
037,
U.
S.
Environmental
Protection
Agency,
Research
Triangle
Park,
NC,
June
1974.

2.
R.
J.
Dyck
and
J.
J.
Stukel,
"
Fugitive
Dust
Emissions
From
Trucks
On
Unpaved
Roads",
Environmental
Science
And
Technology,
10(
10):
1046­
1048,
October
1976.

3.
R.
O.
McCaldin
and
K.
J.
Heidel,
"
Particulate
Emissions
From
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12.
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Emission
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19.
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600/
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84­
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86­
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21.
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87­
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22.
Fugitive
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Available
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EPA­
450/
2­
92­
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