-------
TABLE 4-3. THRESHOLD FRICTION VELOCITIES—INDUSTRIAL AGGREGATES
Threshold wind
Material
Overburden*
Scoria (roadbed
•ateHal)*
Ground coal*
(surrounding coal
Pile)
Uncrusted coal pile*
Scraper tracks on
coal pile**6
Fine coal dust on
concrete padc
Threshold
friction
velocity,
n/s
1.02
1.33
0.55
1.12
0.62
0.54
velocity at
Roughness
height,
on
0.3
0.3
0.01
0.3
0.06
0.2
10 m
actual
21
27
16
23
15
11
(a/s)
fcf,.
19
25
10
21
12
10
Ref.
7
7
7
7
7
12
*Westem surface coal «1ne.
°Llghtly crusted.
CEastern power plant.
4-10
-------
TABLE 4-4. THRESHOLD FRITION VELOCITIES—ARIZONA SITES"
Location
Threshold
friction
velocity,
•/sec
Roughness
height,
(a)
Threshold
wind velocity
•t 10 B.
•/sec
Mesa - Agricultural site 0.57
Glendale - Construction site 0.53
Narlcopa - Agricultural site 0.58
Yona - Disturbed desert 0.32
Yuma • Agricultural site 0.58
Algodones - Dune flats 0.62
Yuna - Scrub desert 0.39
Santa Cruz River, Tucson 0.18
Tucson - Construction site 0.25
AJo • Nine tailings 0.23
Hayden - Nine tailings 0.17
Salt River. Mesa 0.22
Casa Grande - Abandoned 0.25
agricultural land
0.0331
0.0301
0.1255
0.0731
0.0224
0.0166
0.0163
0.0204
0.0181
0.0176
0.0141
0.0100
0.0067
16
15
14
8
17
18
11
5
7
7
5
7
8
4-11
-------
Fof narrowly «l»ed, finely divided materials only
• —1
A00'*0*l* ••*•
dlilrlbiillon mod* n' U«MM»H
Gravel ^
__
Coarsa
Sand H
Fine
Sand "
X-
03
02
•
di
w
005
001
(In) (mm) (cnVs)
0
7
• .6
6
4
3
-
2
_
1
05
t
01
002
-
-
-
-
-
-
-
-
-
ISO
"S""-"**
IM***!.*,*.
U«— —^.
100 C-1.TJ_1
a**«<»4tt.
ssrr*^
so f^S3*«i"
0
Figure 4-2. Scale of threshold friction velocities.
-------
This assumes a typical roughness height of 0.5 on for open terrain.
Equation 4-5 Is restricted to large relatively flat piles or exposed areas
with Uttle penetration -Into the surface wind layer.
If the pile significantly penetrates the surface wind layer (I.e.,
with a helght-to-base ratio exceeding 0.2). It Is necessary to divide the
pile area Into subareas representing different degrees of exposure to
wind. The results of physical •odellng show that the frontal face of an
elevated pile 1s exposed to wind speeds of the sane order as the approach
wind speed at the top of the pile.
For two representative pile shapes (conical and oval with flat-top,
37 degree side slope), the ratios of surface wind speed (u$) to approach
wind speed (ur) have been derived from wind tunnel studies." The results
are shown 1n Figure 4-3 corresponding to an actual pile height of 11 •. a
reference (upwind) anemometer height of 10 «, and a pile surface roughness
height (2Q) of 0.5 OB. The Measured surface winds correspond to a height
of 25 en above the surface. The area fraction within each contour pair 1s
specified 1n Table 4-5.
The profiles of us/ur 1n Figure 4-3 can be used to estimate the
surface friction velocity distribution around.similarly shaped piles,
using the following procedure: ' *
1. Correct the fastest alle value (u*) for the period of Interest
from the anemometer height (z)'to a reference height of 10 n
(uf0) using a variation of Equation 4-2, as follows:
«• . * in go/o.oos)
U'o " u In (z/6.6fl5J
where a typical roughness height of 0.5 en (0.005 •) has been
assumed.' If a site specific roughness height 1s available, 1t
should be used.
2. Use the appropriate part of Figure 4-3 based on the pile shape
and orientation to the fastest mile of wind, to obtain the
corresponding surface wind speed distribution (U), I.e.,
4-13
-------
Flow
, Direction
Pile A
Pile B1
Pile B2
Pile B3
Figure 4-3. Contours of normalized surface wind
speeds, us/ur.
-------
TABLE 4-5. SUBAREA DISTRIBUTION FOR REGIMES OF us/ur
Percent of pile surface area (Figure 4-3)
Pile subarea Pile A '• Pile Bl Pile 82 Pile B3
0.2a 5533
0.2b 35 2 28 25
0.2c 29
0.6a 48 26 29 28
~0.6b - 24 22 26
0.9 12 14 15 14
1.1 3 4
4-15
-------
3. For any subarea of the pile surface having a narrow range of
surface wind speed, use e variation of Equation 4-2 to calculate
the equivalent friction velocity (u*). as follows:
0.4 u*
u* • K* • 0.10 u* (4-8)
From this point on, the procedure 1s Identical to that used for a
flat pile, as described above.
Implementation of the above procedure 1s carried out 1n the following
steps:
1. Determine threshold friction velocity for credible material of
Interest (see Tables 4-3 and 4-4 or Figure 4-2 or determine from mode of
aggregate size distribution).
2. Divide the exposed surface area Into subareas of constant
frequency of disturbance (N).
3. Tabulate fastest mile values (u*) for each frequency of
disturbance and correct them to 10 m (ut«) using Equation 4-6.
4. Convert fastest mile values (ut«) to equivalent friction
velocities (u*), taking Into account (a) the uniform wind exposure of
nonelevated surfaces, using Equation 4-5, or (b) the nonunlfonn wind
exposure of elevated surfaces (piles), using Equations 4-7 and 4-8.
5. For elevated surfaces (piles), subdivide areas of constant N Into
subareas of constant u* (I.e., within the Isopleth values of u$/ur 1n
Figure 4-3 and Table 4-5) and determine the size of each subarea.
6. Treating each subarea (of constant N and u*) as a separate
source, calculate the erosion potential (P^) for each period between
disturbances using Equation 4-4 and the emission factor using
Equation 4-3.
7. Multiply the resulting emission factor for each subarea by the
size of the subarea. and add the emission contributions of all subareas.
Note that the highest 24-h emissions would be expected to occur on the
windiest day of the year. Maximum emissions are calculated assuming a
single wind event with the highest fastest mile value for the annual
period.
4-16
-------
The reconnended emission facto- equation presented above assumes that
all of the erosion potential corresponding to the fastest mile of wind Is
lost during the period between disturbances. Because the fastest mile
event typically lasts only about 2 Bin, which corresponds roughly to the
half-life for the decay of actual erosion potential, 1t could be argued
that the emission factor overestimates part 1cu late Missions. However,
there are other aspects of the wind erosion process which offset this
apparent conservatism:
1. The fastest Bile event contains peak winds which substantially
exceed the mean value for the event.
2. Whenever the fastest mile event occurs, there are usually a
number of periods of slightly lower mean wind speed which contain peak
gusts of the same order as the fastest mile wind speed.
Of greater concern 1s the likelihood of overpredlctlon of wind
erosion emissions 1n the case of surfaces disturbed Infrequently 1n
comparison to the rate of crust formation.
4.1.3 Wind Emissions From Continuously Active Piles
For emissions from wind erosion of active storage piles, the
following total suspended part 1cu late (TSP) emission factor equation 1s
recommended:
(4-9)
where: E • total suspended paniculate emission factor
s • silt content of aggregate, percent
p • number of days with ±0.25 am (0.01 in.) of precipitation per
year
f • percentage of time that the unobstructed wind speed exceeds
5.4 m/s (12 mph) at the mean pile height
The fraction of TSP which Is PM,0 is estimated at 0.5 and 1s
consistent with the PM;-8/TSP ratios for materials handling (Section 4.1.1)
and wind erosion (Section 4.1.2). The coefficient 1n Equation (4-9) 1s
taken from Reference 1, based on sampling of emissions from a sand and
4-17
-------
gravel storage pile area during periods when transfer and maintenance
equipment was not operating. The factor from Reference 1, expressed 1n
•ass per unit area per day. 1s wort reliable than the factor expressed 1n
•ass per unit mass of Mterial placed 1n storage, for reasons stated 1n
that report. Note that the coefficient has been halved to adjust for the
estimate that the wind speed through the emission layer at the test site
was one half of the value measured above the top of the piles. The other
terns 1n this equation were added to correct for silt, precipitation, and
frequency of high winds, as discussed 1n Reference 2. Equation (4-9) 1s
rated In AP-42 as C for application In the sand and gravel Industry and 0
for other Industries (see Appendix A).
Worst case emissions from storage pile areas occur under dry windy
conditions. Worst case emissions from materials handling (batch and
continuous drop) operations may be calculated by substituting Into
Equation (4-9) appropriate values for aggregate material moisture content
and for anticipated wind speeds during the worst case averaging period,
usually 24 h. The treatment of dry conditions for vehicle traffic
(Section 3.0) and for wind erosion (Equation. 4-9), centering around
parameter p. follows the methodology described 1n Section 3.0. Also, a
separate set of none11mat1c correction parameters and source extent values
corresponding to higher than normal storage pile activity may be justified
for the worst case averaging period*
4.2 DEMONSTRATED CONTROL TECHNIQUES
The control techniques applicable to storage piles fall Into distinct
categories as related to materials handling operations (Including traffic
around piles) and wind erosion. In both cases, the control can be
achieved by (a) source extent reduction, (b) source Improvement related to
work practices and transfer equipment (load-In and load-out operations),
and (c) surface treatment. These control options are summarized In
Table 4-6. The efficiency of these controls ties back to the emission
factor relationships presented earHer.in this section.
. •*
In most cases, good work practices which confine freshly exposed
material provide substantial opportunities for emission reduction without
the need for Investment 1n a control application program. For example,
pile activity, loading and unloading, can be confined to leeward
(downwind) side of the pile. This statement also applies to areas around
4-18
-------
• Topsoll removal: 5.7 kg./^T for pan scrapers
• Earthmovlng: 1.2 kg/VICT for pan scrapers
• Truck haulage: ' 2.8 kg/VKT for haul trucks
PH10 emissions due to Materials handling and wind erosion of exposed areas
can be calculated using the emission factors presented In Sections 4.0 and
6.0, respectively.
5.1.2 Demolition Emissions
For demolition sites, the operations Involved 1n demolishing and
removing structures from a site are:
• Mechanical or explosive dismemberment
• Debris loading
• Onslte truck traffic
• Pushing (dozing) operations
5.1.2.1 Dismemberment. Since no emission factor data are available
for blasting or wrecking a building, the first operation 1s addressed
through the use of the revised AP-42 nateHals handling equation:3,*
Ej - k(0.0016) 1 4 (5-1)
(?) '
where EQ • PM10 emission factor In kg/Mg of material
k • particle size multiplier'- 0.35 for PH10
U • mean wind speed In «/s (default • 2.2 m/s)
M • material moisture content 1n percent (default « 2 percent)
and EQ • 0.00056 kg/Mg (with default parameters)
The above factor can be modified for waste tonnage related to
structural floor space where 1 m* of floor space represents 0.45 Mg of
waste material (0.046 ton/ft:).' The revised emission factor related to
structural floor space (using default parameters) can be obtained by:
En • 0.00056 kg/Mg • °'45 **
•'
- 0.00025 kg/m:
5-3
-------
5.1.2.3 Debris Loading. Tht Emission factor for debris loading 1s
based on two tests of the filling of trucks with crushed limestone using a
front-end loader which Is part of the test basis for the batch drop
equation 1n AP-42, § 11.2.3.* The resulting emission factor for debris
loading 1s:J
0.45
E. * k(0.029) kg/Mg
*• •»
• 0.0046 kg/m»
where 0.029 kg/Mg 1s the average measured TSP emission factor and k 1s the
particle size multiplier (0.35 for PM10).
5.1.2.4 Ons 1te Truck Traffic. Emissions from onslte truck traffic
1s generated from the existing AP-42 unpaved road equation presented 1n
Section 3.0 above. >
E • 1-7 k (HjHry) ((S} (5-2)
where E • PM,0 emission factor 1n kg/vehicle kilometer traveled (VKT)
k • particle size multiplier • 0.36 for PN10 .
s « silt content In percent (default • 12 percent)
S • truck speed 1n km/h (defaglt » 16 kra/h)
U - truck weight 1n Mg (default • 20 Hg)
w - number of truck wheels (default • 10 wheels)
p « number of days with measurable precipitation
(default « 0 days)
and ET • 1.3 kg/VKT (with default values)
The above factor 1s converted from kg/VKT to kg/m* of structural
f-loor space by:J
£ m 0.40 km . 1 m3 waste . 7.65 mi volume . 1.3 kg
23 mJ waste 4 m' volume 0.836 m* floor space VKT
« 0.052 kg/mi
5.1.2.5 Pushing Operations. For pushing (bulldozer) operations, the
AP-42 emission factor equation for overburden removal at Western surface
5-4
-------
coal mines can be used.' Although this equation actually relates to par-
tlculate <15 umA, 1t would be expected that the PM10 emissions from such
operations would be generally comparable. The AP-42 dozer equation 1s:
,
P (M)1'4
where Ep • PM,0 emission rate 1n kg/h
S • silt content of surface material 1n percent
«
(default « 6.9 percent)
M • moisture content of surface material 1n percent
(default • 7.9 percent)
and Ep • 0.45 kg/h (with default parameters)
Finally. PH,0 emissions due to wind erosion of exposed areas can be
calculated as discussed 1n Section 6.0. In general, these emissions are
expected to be minor as compared to other sources.
5.1.3 Mud/Dirt Carryout Emissions
Finally, the Increase 1n emissions on paved roads due to mud/dirt
carryout have been developed based on surface loading measurements at
eight sites.* Tables 5-2 and 5-3 provide these emission factors in terms
of gm/vehlcle pass which represent PM;0 generated over and above the
•background* for the paved road sampled. Table 5-2 expresses the emission
factors according to the volume of traffic entering and leaving the site
whereas Table 5-3 expresses the same data according to type of
construction.
5.2 DEMONSTRATED CONTROL TECHNIQUES •
As discussed above, similar generic open dust sources "exist at both
construction and demolition sites. Therefore, similar types of controls
would also apply. In this section, a' discussion 1s provided on the
various tecnnlques available for the control of open dust sources
associated with construction and demolition. Detailed Information on
control efficiency, Implementation cost, etc., will be presented in
Section 5.3 below.
5-5
-------
TABLE 5-2. EMISSIONS INCREASE UE) BY SITE TRAFFIC VOLUME* '
Sites with >25
Particle
size .
fraction"
<-30 uB
<10 UB
<2.5 u>
Mean,
X
52
13
5.1
Standard
devia-
tion, 0
28
6.7
2.6
veh1de/d
Range
15-80
4.4-20
1.7-7.8
Sites
Mean,
X
19
5.5
2.2
with <25 veh1cle/d
Standard
devia-
tion, v
7.8
2.3
0.88
Range
14-28
4.2-8.1
1.6-3.2
*aE expressed In g/veh1c1e pass.
Aerodynamic diameter.
TABLE -5-3.
Particle
size
fraction0
<-30 uB
<10 uB
Mean,
X
65
16
<2.5 uffl 6.3
EMISSIONS
Conmerdal
Standard
devia-
tion, 0
39
9.3
3.6
INCREASE* (AE)
Range
15-110
4.2-25
1.6-9.7
BY CONSTRUCTION TYPE*
Mean,
X
39
10
3.9
Residential
Standard
devia-
tion, o
»•
22
5.4
2.1
Range
10-72
2.8-19
1.1-7.3
*iE expressed 1n g/vehlcle pass.
"Aerodynamic diameter.
5-6
-------
6.0 OPEN AREA WIND EROSION
Oust emissions My be generated by wind erosion of open agricultural
land or exposed ground areas on public property or within an Industrial
facility.
With regard to estimating part1culate emissions from wind erosion of
exposed surface material, site Inspection can be used to determine the
potential for continuous wind erosion. The two basic requirements for
wind erosion are that the surface be dry and exposed to the wind. For
example. 1f the contaminated site lies 1n a swampy area or 1s covered by
unbroken grass, the potential for wind erosion 1s virtually nil. If. on
the other hand, the vegetative cover 1$ not continuous over the exposed
surface, then the plants are considered to be nonerodlble elements which
absorb a fraction of the wind stress that otherwise acts to suspend the
Intervening soil.
For estimating emissions from wind erosion, either of two emission
factor equations are recommended depending on the credibility of the
surface material. Based on the site survey, the exposed surface must be
placed 1n one of two credibility classes described below. -The division
between these classes 1s best defined 1n terms of the threshold wind speed
for the onset of wind erosion.
Nonhomogeneous surfaces Impregnated with nonerodlble elements
(stones, clumps of vegetation, etc.) are characterized by the finite
availability ("limited reservoir") of credible material. Such surfaces
have high threshold wind speeds for wind erosion, and part 1culate emission
rates tend to decay rapidly during an erosion event. On the other hand,
bare surfaces of finely divided material such as sandy agricultural soil
are characterized by an "unlimited reservoir" of credible, particles. Such
surfaces have low threshold wind speeds for wind erosion, and partlculate
emission rates are relatively time independent at a given wind speed.
For surface areas not covered by continuous vegetation, the
classification of surface material as either having a "limited reservoir"
or an "unlimited reservoir' of erodlble surface particles Is determined by
estimating the threshold friction velocity. Based on analysis of wind
erosion research, the dividing line for the two credibility classes 1s a
6-1
Preceding page blank
-------
threshold friction velocity of about 50 on/s. This somewhat arbitrary
division 1s based on the observatlcr. that highly credible surfaces;
usually corresponding to sandy surface soils that are fairly deep, -have
threshold friction velocities below 50 cn/s. Surfaces with friction
velocities larger than 50 cn/s tend to be composed of aggregates too large
to be eroded «1xed 1n with a snail amount of credible Material or of
crusts that are resistant to erosion.'
The cutoff friction velocity of 50 on/s corresponds to an ambient
wind speed of about 7 •/$ (15 mph), measured at a height of about 7 m. In
turn, a specific value of threshold friction velocity for the credible
surface Is needed for either wind erosion emission factor equation
(model).
Crusted surfaces are regarded as having a 'limited reservoir" of
credible particles. Crust thickness and strength should be examined
during the site Inspection, by testing with a pocket knife. If the crust
1s more than 0.6 cm thick and not easily crumbled between the fingers
(modulus of rupture >1 bar), then the soil may be considered non-
erodlble. If the crust thickness Is less than 0.6 cm or 1s easily
crumbled, then the surface should be treated as having a limited reservoir
of credible particles. If a crust 1s found beneath a loose deposit, the
amount of this loose deposit, which constitutes the limited erosion
reservoir, should be carefully estimated.
For uncrusted surfaces, the threshold friction velocity 1s best
estimated from the dry aggregate structure of the soil. A simple hand*
sieving test of surface soil 1s highly desirable to determine the mode of
the surface aggregate size distribution by Inspection of relative sieve
catch amounts, following the procedure specified 1n Figure 6-1. The
threshold friction velocity for erosion can be determined from the mode of
the aggregate size distribution, following a relationship derived by
Gillette (1980) as shown 1n Figure 6-1.-'
A more approximate basis for determining threshold friction velocity
would be based on hand sieving with just one sieve, but otherwise follows
the procedure specified in Figure 6-2. Based on the relationship
developed by Blsal and Ferguson (1970), 1f more than 60 percent of the
5-2
-------
I
CJ
u
01
U
o
I
u
•r-
U.
•a
*o
i/»
•i
IOOO
lop
10
I «••*••!
0.1
I 10
Aggregate Size Distribution Mode (m)
100
Figure 6-1. Relationship of threshold friction velocity to size distribution node.
-------
FIELD PROCEDURE FOR DETERMINATION OF THRESHOLD FRICTION VELOCITY*
1. PREPARE A NEST OF SIEVES WITH THE FOLLOWING OPENINGS: 4 on, 2 •».
1 on. 0.5 on. 0.25 m. PLACE A COLLECTOR PAN BELOW THE BOTTOM SIEVE
(0.25-MB OPENING).
2. COLLECT A SAMPLE REPRESENTING THE SURFACE LAYER OF LOOSE PARTICLES
(APPROXIMATELY 1 on IN DEPTH FOR AN UNCRUSTEO SURFACE). REMOVING ANY
ROCKS LARGER THAN ABOUT 1 on IN AVERAGE PHYSICAL DIAMETER. THE AREA
TO BE SAMPLED SHOULD NOT BE LESS THAN 30 Oi x 30 ou
3. POUR THE SAMPLE INTO THE TOP SIEVE (4-on. OPENING). AND PLACE A LID ON
THE TOP.
4. ROTATE THE COVERED SIEVE/PAN UNIT BY HAND USING BROAD SWEEPING ARM MO-
TIONS IN THE HORIZONAL PLANE. COMPLETE 20 ROTATIONS AT A SPEED JUST
NECESSARY TO ACHIEVE SOME RELATIVE HORIZONTAL MOTION BETWEEN THE SIEVE
AND THE PARTICLES. - '
5. INSPECT THE RELATIVE QUANTITIES OF CATCH WITHIN EACH SIEVE AND
DETERMINE WHERE THE MODE IN THE AGGREGATE SIZE DISTRIBUTION LIES.
I.E.. BETWEEN THE OPENING SIZE OF THE SIEVE WITH THE LARGEST CATCH AND
THE OPENING SIZE OF THE NEXT LARGEST SIEVE.
•ADAPTED FROM A LABORATORY PROCEDURE PUBLISHED BY W. S. CHEPIL (1952).*
Figure 6-2.
6-4
-------
soil passes a 1-an sieve, the "unlimited reservoir" node! will apply; If
not, the 'United reservoir" model iill apply.* This relationship has
been verified by Gillette (1980) on desert soils.*
If the soil contains nonerodlble elements which are too large to
Include In the sieving (I.e., greater than about 1 OB In diameter), the
effect of these elements must be taken Into account by Increasing the
threshold friction velocity. Marshall (1971) has employed wind tunnel
studies to quantify the Increase 1n the threshold velocity for differing
kinds of nonerodlble elements." His results are depicted In terms of a
graph of the rate of corrected to unconnected friction velocity versus Lc
(Figure 6-3). where 1^ 1s the ratio of the silhouette area of the
roughness elements to the total area of the bare loose soil. The
silhouette area of a nonerodlble element Is the projected frontal area
normal to the wind direction.
A value for LC Is obtained by marking off a l-m x l-« surface area
and determining the fraction of area, as viewed from directly overhead,
that 1s occupied by nonerodlble elements. Then the overhead area should
be corrected to the equivalent frontal area; for example. If a spherical
nonerodlble element 1s half embedded 1n the surface, the frontal area 1s
one-half of the overhead area. Although 1t 1s difficult to estimate Lc
for values below 0.05, the correct1on-to-fr1ct1on velocity becomes less
sensitive to the estimated value of Lg.
The difficulty 1n estimating Lg also Increases for small nonerodlble
elements. However, because small nonerodlble elements are more likely to
be evenly distributed over the surface, 1t 1s usually acceptable to
examine a smaller surface area, e.g., 30 cm x 30 cm.
Once again, loose sandy soils fall into the high credibility
("unlimited reservoir") classification. These soils do not promote crust
formation, and show only a brief effect of moisture addition by
rainfall. On the other hand, compacted soils with a tendency for crust
formation fall Into the low ("limited reservoir") credibility group. Clay
content In soil, which tends to promote crust formation, 1s evident from
crack formation upon drying.
6-5
-------
2 3 45*7891 . 2 J 4S67I9I
2 3 4 3 • 7 B4l
TJ
OJ
4J
U
O»
(U
o
c
13
10
Figure 6-3. Increase in threshold friction velocity with LC.
-------
The roughness height, z0, which 1s related to the size and spacing of
surface roughness elements, 1s needcu to convert the friction velocity to
the equivalent wind speed at the typical weather station sensor height of
7 • above the surface. Figure 6-4 depicts the roughness height scale for
various conditions of ground cover.* The conversion to the 7-9 value 1s
discussed below.
6.1 ESTIMATION OF EMISSIONS
6.1.1 'Limited* Erosion Potential
In the case of surfaces characterized by a *11i1ted reservoir" of
credible particles, even the highest Kan atmospheric wind speeds are
usually not sufficient to sustain wind erosion. However, wind gusts Bay
quickly deplete a substantial portion of the erosion potential. Because
erosion potential has been found to Increase rapidly with Increasing wind
speed, estimated emissions should be related to the gusts of highest
•agnltude.
The routinely measured Meteorological variable which best reflects
the Magnitude of wind gusts 1s the fastest Mile. This quantity represents
the wind speed corresponding to the whole Mile of wind Movement which has
passed by the 1-«1 contact anemometer 1n the least amount of tine. Dally
Measurements of the fastest Mile are presented 1n the monthly Local CUma-
tologlcal Data (LCD) summaries. The LCD summaries May be obtained from
the National Climatic Center, Ashevllle, North Carolina. The duration of
the fastest mile, typically about 2 «1n (for a fastest «1le of 30 nph).
matches well with the half life of the erosion process, which ranges
between 1 and 4 irln. It should be noted, however, that peak winds can
significantly exceed the dally fastest «1le.
The wind speed profile 1n the surface boundary layer 1s found to
follow a logarithmic distribution:
u(2) • o 1n {- (z " V <6-^
-*. o
where: u • wind speed, on/s
u* • friction velocity, cm/s
%
z • height above test surface, cm
_Z0 • roughness height, en
0.4 • von (Carman's constant, dlmenslonless
6-7
-------
High Rise Buildings
(30+Floors)
Suburban
Medium Buildings
(Institutional)
u
O
Suburban
Residential Dwellings '
Wheat Field.
O-
Plowed Field
Zo (cm)
1000
Natural Snow
*
BOO
600
100
—200—
100 .
—80
—60
-40
-20
io
™^*o»
mt^^^3 •
— 2
1.
— 0.
— 0
— 0.
— 0.
0.
.0—
.0—
.0—
.oJ
.0
0—
0—
0
8—
4
2
1
Urban Area
Woodland Forest
Grassland
Figure 6-4. Roughness heights for various surfaces.
6-8
-------
The friction velocity (u+) 1r a measure of wind shear stress on the
credible surface, as determined from the slope of the logarithmic velocity
profile. The roughness height (zo) 1s a Measure of the roughness of the
exposed surface as determined from the y-Intercept of the velocity
profile. I.e., the height at which the wind speed 1s zero. These
parameters are Illustrated 1n Figure 6-5 for a roughness height of 0.1 on.
Emissions generated by wind erosion are also dependent on the
frequency of disturbance of the credible surface because each time that a
surface 1s disturbed. Its erosion potential 1s restored. A disturbance 1s
defined as an action which results 1n the exposure of fresh surface
material. On a storage pile, this would occur whenever aggregate material
1s either added to or removed from the old surface. A disturbance of an
exposed area may also result from the turning of surface material to a
depth exceeding the size of the largest pieces of material present.
The emission factor for wind-generated partleulate emissions from
mixtures of credible and nonerodlble surface material subject to
disturbance may be expressed In units of g/m*-yr as follows:
N
Emission factor • k. J P< (6-2)
where: k • particle size multiplier
N • number of disturbances per year
Pj • erosion potential corresponding to the observed (or probable)
fastest mile of wind for the 1th period between disturbances,
g/m'
The particle size multiplier (k) for Equation 6-2 varies with
aerodynamic panicle size, as follows:
AERODYNAMIC PARTICLE'SIZE MULTIPLIERS FOR EQUATION 6-2
<3D tun <15 urn <10 um <2.5 K»
TTo 57? 575 572
6-9
-------
AatTHMK nc
to*
torn
Srceo AT Z
•Sreeo AT tOm
Figure 6-5. Illustration of logarithmic velocity profile.
-------
This distribution of particle s1?^ within the <30 urn fraction 1s
comparable to the distributions reported for other fugitive dust sources
where wind speed 1s • factor. This 1s Illustrated, for example. 1n the
distributions for batch and continuous drop operations enconpasslng a
number of test aggregate materials (see AP-42 Section 11.2.3).
In calculating emission factors, each area of an credible surface
that Is subject to a different frequency of disturbance should be treated
separately. For a surface disturbed dally, N • 365/yr. and for a surface
«
disturbance once every 6 mo, N • 2/yr.
The erosion potential function for a dry, exposed surface has the
following font:
P • 58 (u* - u*)» * 25 (u* - u*)
P • 0 for u* s uj
where: u* • friction velocity (•/$)
u£ • threshold friction velocity (•/$)
Because of the nonlinear fora of the erosion potential function, each
erosion event must be treated separately.
Equations 6-2 and 6-3 apply only to dry, exposed materials with
limited erosion potential. The resulting calculation 1s valid only for a
time period as long or longer than the period between disturbances.
Calculated emissions represent Intermittent events and should not be Input
directly Into dispersion models that assume steady state emission rates.
For uncrusted surfaces, the threshold friction velocity Is best
estimated from the dry aggregate structure of the soil. A simple hand
sieving test of surface soil (adapted from a laboratory procedure
published by W. S. Chepll*) can be used, to determine the mode of the
surface aggregate size distribution by Inspection of relative sieve catch
amounts, following the procedure specified 1n Figure 6-2. The threshold
friction velocity for erosion can be determined from the mode of the
aggregate size distribution, as described by Gillette.* This conversion
1s presented 1n Figure 6-1.
6-11
-------
Threshold friction velocities s'or several surface types have been
determined by field measurements with a portable wind tunnel. These
values are presented 1n Tables 6-1 and 6-2 and Figure 6-6.
The fastest «11e of wind for the periods between disturbances Bay be
obUlned froo the Monthly LCD suonaHes for the nearest reporting weather
station that 1s representative of the site In question.7 These summaries
report actual fastest mile values for each day of a given month. Because
the erosion potential 1s a highly nonlinear function of the fastest alle.
wan values of the fastest mile are Inappropriate. The anemometer heights
of reporting weather stations are found 1n Reference 8, and should be
corrected to a 10 • reference height using Equation 6-1.
To convert the fastest mile of wind (u*) from a reference anemometer
height of 10 m to the equivalent friction velocity (u*), the logarithmic
wind speed profile Bay be used to yield the following equation:
u* • 0.053 uto (6-4)
where: u* • friction velocity (•/$)
uf0 • fastest mile of reference anemometer for period between
disturbances (a/s)
This assumes a typical roughness -height of 0.5 on for open terrain.
Equation 6-4 Is restricted to large relatively flat areas with little
penetration Into the surface wind layer.
Implementation of the above procedure 1s carried out 1n the following
steps:
1. Determine threshold friction velocity for erodlWe material of
Interest (see Tables 6-1 and 6-2 and Figure 6-6 or determine from
mode of aggregate size distribution).
2. Divide the exposed surface area Into subareas of constant
frequency of disturbance (N).
3. Tabulate fastest mile values (u*) for each frequency of
disturbance and correct them to 10 m (uto) using Equation 6-5.
6-12
-------
TABLE 6-1. THRESHOLD DICTION VELOCITIES
•
Material
Overburden*
Scoria (roadbed
material)*
Ground coal
(surrounding coal
pile)
Uncrusted coal p11ea
Scraper tracks on
coal pile*'6
Fine coal dust on
concrete padc
Threshold
friction
velocity
(•/$)
1.02
1.33
0.55
1.12
0.62
0.54
Roughness
height
0.3
0.3
0.01
0.3
0.06
0.2
Threshold
wind
velocity at 10 n (m/s)
Z0 • Actual Zg
21
27
16
23
15
11
• 0.5 cm
19
25
10
21
12
10
Ref.
2
2
2
2
2
3
fwestem surface coal «1ne.
"Lightly crusted.
Eastern power plant.
6.13
-------
TABLE 6-2. THRESHOLD FRICTION VELOCITIES—ARIZONA SITES
Location
Threshold
friction
velocity,
•/sec
Roughness
height.
Threshold
velocity
at 10 m.
•/sec
Mesa - Agricultural site 0.57
Glendale - Construction site 0.53
Marlcopa - Agricultural site 0.53
Yuaa • Disturbed desert 0.32
YUM - Agricultural site 0.58
Algodones - Dune flats 0.62
YUM - Scrub desert 0.39
Santa Cruz River, Tucson 0.18
Tucson - Construction site 0.25
Ajo - Nine tailings 0.23
Nayden - Mine tailings 0.17
Salt River. Mesa 0.22
Casa Grande - Abandoned 0.25
agricultural land
0.0331
0.0301
0.1255
0.0731
0.0224
0.0166
0.0163
0.0204
0.0181
0.0176
0.0141
0.0100
0.0067
16
15
14
8
17
18
11
5
7
7
5
7
8
6-14
-------
For narrowly sited. (Inety divided malarial* only
I
t^
01
dlilflbullon
^
Gravel ^
-
M
_ j
Coarse
Sand ~*
» ^_ w. -•• •.. .^ M^ ..^ _ .«
Flno
Snnd "
—
03
02
01
k*
DOS
001
^~
(In)
- -.'«
7
6
5
4
3
-
2
—
1
0.6
-
,01
002
(mm)
-
-
-
•
_
-
—
—
-
•"
Ja_ MeaiMted
- ISO
- 100
- so
ri«tlwMp«Mii
•41M4
Jfl
Figure 6-6. Scale of threshold friction velocities.
-------
4. Convert fastest mile values (uf0) to equivalent friction
velocities (u*), using Equation 6-4.
5. Treating each subarea (of constant H and u*) as a separate
source, calculate the erosion potential (Pj) for each period
between disturbances using Equation 6-3 and the emission factor
using Equation 6-2.
6. Multiply the resulting emission factor for each subarea by the
size of the subarea. and add the emission contributions of all
subareas. Note that the highest 24-h ealsslons Mould be expected
to occur on the windiest day of the year. Max1BUD emissions are
calculated assualng a single wind event with the highest fastest
•He value for the annual period.
The recoonended emission factor equation presented above assumes that
all of the erosion potential corresponding to the fastest rile of wind 1s
lost during the period between disturbances. Because the fastest mile
event typically lasts only about 2 Bin. which corresponds roughly to the
half-life for the decay of actual erosion potential. It could be argued
that the emission factor overestimates partlculate emissions. However,
there-are other aspects of the wind erosion process which offset this
apparent conservatism:
1. The fastest mile event contains peak winds which substantially
exceed the mean value for the event.
2. Whenever the fastest mile event occurs, there are usually a
number of periods of slightly lower mean wind speed which contain
peak gusts of the same order as the fastest mile wind speed.
Of greater concern 1s the likelihood of overpred1ct1on of wind
erosion emissions 1n the case of surfaces disturbed Infrequently 1n
comparison to the rate of crust formation.
6.1.2 "Unlimited' Erosion Potential
For surfaces characterized by an "unlimited reservoir* of credible
particles, partlculate emission rates are relatively time Independent at a
given wind speed. The technology currently used for predicting
agricultural wind erosion 1n the United States 1s based on variations of
the Wind Erosion Equation.*;,»» This prediction system uses erosion loss
estimates that are Integrated over large fields and long-time scales to
6-16
-------
7.0 AGRICULTURE
Fugitive dust from agricultural operations Is suspected of
contributing significantly to the ambient part1culate levels of many
agricultural counties. Such agricultural operations Include (a) plowing,
(b) disking, (c) fertilizing, (d) applying herbicides and Insecticides,
(e) bedding, (f) flattening and fining beds, (g) planting, (h) culti-
vating, and (1) harvesting. These operations can be generlcally
classified as soil preparation, soil maintenance, and crop harvesting
operations. As discussed In Section 6« dust Missions are also generated
by wind erosion of bare or partially vegetated soil. This section will
focus on emissions fron both wind erosion and agricultural tilling opera-
tions that are designed to (a) create the desired soil structure for the
crop seed bed and (b) to eradicate weeds.
7.1 ESTIMATION OF EMISSIONS
7.1.1 Tilling
The mechanical tilling of agricultural land Injects dust particles
Into the ataosphere as the soil Is loosened or turned under by plowing.
disking, harrowing, one-way1ng, etc. AP-42 presents a predictive emission
factor equation for the estimation of dust emissions from agricultural
tilling:*
E - k(5.38)(s)«-« kg/ha •
E • k(4.80)(s)o*« Ib/acre
where: s • silt content (percent) of surface soil (default value of
18 percent)
k • particle size multiplier (dimenslonless)
The particle size multiplier, k Is given as 0.21 for PM,O. The above
equations are based solely on field testing information cited 1n AP-42.
Silt content of tested soils ranged from 1.7 to 88 percent.
7-1
-------
7.1.2 Hind Erosion
The technology currently used for predicting agricultural wind
erosion 1n the United States 1s based on variations of the Wind Erosion
Equation.».» This prediction system uses erosion loss estimates that are
Integrated over large fields and long time scales to produce average
annual values.
* 7.1.2.1 Simplified Version of Wind Erosion Equation. Presented
below 1s a procedure for estimating windblown or fugitive dust emissions
from agricultural fields. The overall approach and much of the data have
been adapted from the wind erosion equation, which was developed as the
result of nearly 40 yr of research by the U.S. Department of Agriculture
to predict topsoll losses from agricultural fields.
Several simplifications have also been Incorporated during the
adaptation process. The simplified format 1s not expected to affect
accuracy 1n Its present usage, since wind erosion estimates using the
simplified equation are almost always within 5X of those obtained with the
original USOA equation. Most of the Input data are not accurate to ±5X.
7.1.2.1.1 Windblown dust equation. .The modified equation 1s of the
form:
E « kalKCL'V (7-1)
where: E • PMi0 wind erosion losses of tilled fields, tons/acre/yr
k • 0.5, the estimated fraction of TSP which 1s PM10
a • portion of total wind erosion losses that would be measured
as suspended part1culate, estimated to be 0.025
I « soil credibility, tons/acre/yr
K • surface roughness factor, d1mens1on1ess
C • climatic factor, dlmenslonless
L' • unsheltered field width factor, dlmenslonless
V » vegetative cover factor, dlmenslonless
As an aid In understanding the mechanics of this equation, "I" may be
thought of as the basic credibility of a flat, very large, bare field 1n a
climate highly conducive to wind erosion (I.e.. high wind speeds and
temperature with little precipitation) and 1C, C. L', and V as reduction
7-2
-------
factors for • ridged surface, a c11mat« less conducive to wind erosion,
smaller-sized fields, and vegetative cover, respectively.
The sane equation can be used to estimate emissions from: (1) a
single field, (2) a medium-sized area such as a valley or county, or
(3) an entire AQCR or state. Naturally, more generalized Input data must
be used for the larger land areas, and the accuracy of the resulting
estimates decreases accordingly.
7.1.2.1.2 Procedures for compiling Input data. Procedures for
quantifying the five variable factors 1n Equation (7-1) are explained 1n
detail below.
Soil Credibility. I. Soil credibility by wind Is a function of the
amount of credible fines In the soil. The largest soil aggregate size
normally considered to be erodlble 1s approximately 0.84 ma equivalent
diameter. Soil credibility. I. 1s related to the percentage of dry
aggregates greater than 0.84 mm as shown 1n Figure 7-1. The percentage of
nonerodlble aggregates (and by difference the amount of fines) 1n a soil
sample can be determined experimentally by a standard dry sieving
procedure, using a No. 20 U.S. Bureau of Standards sieve with 0.84-mm
square openings.' .
For areas larger than can be field sampled for soil aggregate size
(e.g.. a county) or In cases where soil particle size distributions are
not available, a representative value of I for use 1n the windblown dust
equation can be obtained from the predominant soil type(s) for farmland 1n
the area. Measured credibilities of various soil textural classes are
presented 1n Table 7-1.
If an area Is too large to be accurately represented by a soil class
or by the weighted average of several soil classes, the map In Figure 7-2
and the legend In Table 7-2 can be used to Identify major soil deposits
and average soil credibility on a national basis. Other soil maps are
available from*the Soil Conservation Service branch of the U.S. Department
of Agriculture.
Values of I obtained from Figure 7-1, from Table 7-1. or from soil
maps can be substituted directly into Equation (7-1).
7-3
-------
- •
' i
;
i
i
m**t\
j 'B°
1
1 ...»
< i
"
.-r
i
M
10
T
;
\
i\
— : •
i
• -
t
..; .
— •
-f-}.'
1
: 1
1
\
• ^
._...
-
•
• ; • . .
: ! ! ' '- !
: i " 1'.
i i '
; '
i • |
-
.
! ,
...j : .
• i ; 1 "~ !
t
. • • , • ~
»
; i
. _ i .. i
1 ' '
. : ...
1 1
\ !i""
\
1
\
• :
•V
: ! N
i
•
-
i
t
...
) 20 36 [_ 4
] rttctU or
.
OTM1M
— ;•
. —
j 1 : 1
'"
v~i':'
•
•
h.. ..
— • —
. :.f~|:---.;
.... -J _... . . _..
—
i...
\ ' !
V *
• l\
. . ..
•
;
\
.. —
...
X ...
.
•
3 • SO 6t> 71
* • wf . . . f *
MT' idu «oeito«Tti
- I .
\
> ; ft
1 •
_. •!_
...J _.
i
*•••
:
:
:
;
' ;
1
i
.. .
,
'
"T
fffv
^
>_[-«b i- id
! .
:' ;
: ... L :-
j
..u_(.._ .
•
1
• 1
i
.
"!' !"";
"H i
.. . .: .J
fe. L.I. ;
• 1 i
I-
Figure 7-1. Soil credibility as a function of particle size.
7-4
-------
TABL- 7-1. SOIL ESSOIBILITY F33. WRIuL'S
SOIL TEXTURAL CLASSES
Predominant soil textural class
Sand*
Loamy sand*
Sandy loan*
Clay
SUty clay
Loam
Sandy clay loam8
Sandy clay*
S1lt loam
Clay loan
Silty clay loam
sm
Credibility, I,
tons/acre/yr
220
134
36
86
86
»
56
56
56
47
47
28
38
*Very fine, fine, or Medium sand.
7-5
-------
CtNtBAt SOIL MAP Of THC UNtTtO STATES
Figure 1-2. Generalized soli map of the United States.
i.
-------
TABLE 7-2. LEG-NO rop SOIL MAP IN ?IGUR£ '-2
Al, A2 Season*!!./ wet soils with subsurface clay accumu-at'c-
A3- AS Cool or cold soils with subsurface clay accumulation
A6- A8 Clays
A9. AID Burnt clay soils
Al)- A13 Dry clay soils with some cementation
01- 06 Arid soils with clay and alkali or caroonate
accumulation
El Poorly.drained loamy sands
E2 Loamy or clayey alluvial deposits
E3- £8 Shallow clay loan deposits on bedrock
E9 Loamy sands 1n cold regions
E10. E12 Loamy sands 1n warn regions
Ell. E13, E14 Loamy sands 1n warn, dry regions
HI, H2 Wet organic soils; peat and muck
II Ashy or amorphous soils 1n cold regions
12 Infertile soils with large amounts of amorphous material
13 Fertile soils of weathered volcanic ash
14 Tundra; frozen soils
15, 16 Thin loam surface horizon soils
*-
17 Clay loams in cool regions
18- 110 Wide varying soil material with some clay t'or
111 Rocky soils shallower than 20 in, to oeorocK
112 Clay loams in warm, moist regions
113 Clay loams In cold regions
•(continued)
7-7
-------
LLL/dl401-7at. D. 3
LE •»-» 'Continued
114 Clay loams 1n temperate climates
Ml- M4 Surface loam horizon underlain by clay
MS Shallow surface loams with no underlying clays
M6- MS Surface loamy soils
M9- M14 Semlarld loams or clay loams
M15. M16 Dry loams
01, 02 Clays and sandy clays
SI- S4 Sandy, clay, and sandy clay loams
Ul Wet silts with some subsurface clay accumulation
U2- U6 S1lty loams with subsurface clay accumulation
U7 Dry silts with thin subsurface clay accumulation
VI- V2 Clays and clay loams
V3- V5 SUty clays
XI- X5 Barren areas, mostly rock with some Included soils
7-3
-------
Surface Roughness Factor. K. Th!s factor accounts for the resistance
to wind erosion provided by ridges and furrows or large clods In the
field. The surface roughness factor. K. 1s a function of the height and
spacing of the ridges, and varies fron 1.0 (no reduction) for a field'with
a saooth surface to a minimum of 0.5 for a field with the optimum ratio of
ridge height (h) to ridge spacing (w).
The relationship between K and h*/w 1s shown 1n Figure 7-3. The
value of K to be used 1n Equation (7-1) should be rounded to the nearest
0.1 because of the large variations Inherent In ridge measurement data.
In cases where there are extreme variations of h or w within a field.
determination of the K value should be United to either 0.5 for a ridge
surface or 1.0 for an unrldged surface.
For county or regional areas, K can best be determined as a function
of crop type, since field preparation techniques are relatively uniform
for a specific crop. Average K values of coonon field crops are shown 1n
Table 7-3. When the K (or L* or V) factors are based on crop type,
separate calculations of windblown dust emissions must be made for each
major crop In the survey area. This procedure Is explained and
demonstrated later 1n this presentation. ' -
Climatic Factor. C. Research has Indicated that the rate of soil
movement by wind varies directly as the cube of wind velocity and
Inversely as the square of soil surface moisture. Surface moisture 1s
difficult to measure directly, but precipitation-evaporation Indices can
be used to approximate the amount of moisture 1n soil surface particles.
Therefore, readily available climatic data can provide a quantitative
Indicator of relative wind erosion potential at any geographic location.
The C factor has been calibrated using the climatic conditions at the
site of much of the research—Garden City, Kansas—as the standard base
(C « 1.00). At any other geographic location, the C factor for use 1n
Equation (7-1) can be calculated as:
u'
C - 0.345 -=—r (7-2)
(PE)
7-9
-------
ff
o
CO
CO
IU
u
ti-
ff
CO
INCHES
Figure 7-3. Determination of surface roughness factor.
7-10
-------
TASLi T-3. VAL'JES *" *., '.. AMH v FOP. CCMMCN -H.D CHOPS
Crop
Alfalfa
Barley
Beans
Corn
Cotton
Grain hays
Oats
Peanuts
Potatoes
R1ce
Rye
Saf flower
Sorghum
Soybeans
Sugar beets
Vegetables
Wheat
• K
1.0
0.6
0.5
0.6
0.5
0.8
0.8
0.6
0.8
0.8
0.6
1.0
0.5
0.6
0.6
0.6
0.6
. L. ft
1000
2000
1000
2000
20CO
2000
2000
1000
1000
1000
2000
2000
2000
2000
1000
500
200C
V, "3/JCrs
30CO
11CO
:so
5:0
2:0
1250
1250
250
400
1000
1250
- 1500
300
250
100
100
1350
Ml
-------
where: W ««ean annual wind velocity, -in mph, corrected to a standard
height of 30 ft
PE * Thomthwalte's precipitation-evaporation Index
• 0.83 (sun of 12 monthly ratios of precipitation to actual
evapotransplratlon)
Monthly or seasonal climatic factors can be estimated from
Equation (7-2) by substituting the Man wind velocity of the period of
Interest for the Man annual wind velocity. The annual PE value 1s used
for all calculations of C.
CIlMtlc factors have been computed froa Heather Bureau data for many
locations throughout the country. Figure 7-4 1s a up showing annual
climatic factors for the USA. C values for use 1n Equation (7-1) may be
taken from appropriate maps like this when preparing regional emission
surveys. For emission estimates covering smaller areas. Equation (7-2)
may be used to obtain C.
Unsheltered Field Width Factor. L'. Soil erosion across a field 1s
directly related to the unsheltered width along the prevailing wind
direction. The rate of erosion 1s zero at the windward edge o.f the field
and Increases approximately proportionately with distance downwind until.
If the field 1s large enough, a maximum'rate of soil movement 1s reached.
Correlation between the width of a field and Its rate of erosion 1s
also affected by the soil credibility of Us surface: the more credible
the surface, the shorter the distance 1n which maximum soil movement 1s
reached. This relationship between the unsheltered width of a field (L),
Us surface credibility (IK), and Us relative rate of soil erosion (L1)
1s shown graphically 1n Figure 7-5. If the curves of Figure 7-5 are used
to obtain the L1 factor for the windblown dust equation, values for the
variables I and K must already be known and an appropriate value for L
must be determined.
L 1s calculated as the distance across the field 1n the prevailing
wind direction minus the distance from the windward edge of the field that
1s protected from wind erosion by a barrier. The distance protected by a
barrier 1s equal to 10 times the height of the barrier, or 10 H. For
example, a row of 30-ft high trees along the windward side of a field
reduces the effective width of the field by 10 x 30 or 300 ft. If the
7-12
-------
SMT|$
ANNUAL CLIMATIC FACTOR C
ORIGINAL DRAWING 4-17-68. 0. V. ARMBRUST.
ARK.. IA., KV., LA.. TCNN.. W. VA. ADDED
11-24-71. N. P. WOODRUFF.
Figure 7-4. Climatic factor used In wind erosion equation.
-------
Figure 7-5. Effect of field length on relative emission rate.
-------
prevailing wind direction differs significantly (more than 25 degrees)
from perpendicularity with the field, L should be Increased to account for
this additional distance of exposure to the wind. The distance across the
field. L 1s equal to the field width divided by the cosine of the angle
between the prevailing wind direction and the perpendicularity to the
field:
For aultlple fields or regional surveys, measurement and calculation
of L values become unwieldy. In region-wide emission estimates, average
field widths should be used. Field width 1s generally a function of the
crop being grown, topography of the area, and the amount of trees and
other natural vegetation 1n or adjacent to the faming areas that would
shelter fields from erosive winds. Since the windblown dust calculations
are already split Into Individual crop type to accurately consider
variations 1n K by crop, average L values have also been developed by
crop; they are presented 1n Table 7-3. These values are representative of
field sizes In relatively flat terrain devoid of tall natural vegetal ton,
such as found 1n large areas of the Great Plains. The L values 1n
Table 7-3 should be divided by 2 1n areas with moderately uneven terrain
and by 3 1n h11ly areas. Additionally, the average field width factors
should be divided by 2 to account for wooded areas and fence thickets
Interspersed with farmland.
Vegetative Cover Factor. V. Vegetative cover on agricultural fields
during periods other than the primary crop season greatly reduces wind
erosion of the soil. This cover most commonly Is crop residue, either
standing stubble or mulched Into the soil. The effect of various amounts
of residue, V, 1^ reducing erosion 1s shown quantitatively In Figure 7-6,
where IKCL' 1s the potential annual soil loss (1n tons/acre/yr) from a
bare field, and V Is the fractional amount of this potential loss which
results when the field has a vegetative cover of V, In lb of a1r-dr1ed
residue/acre. Obviously, the other four variables 1n Equation (7-1)— I,
K, C, and L'— must be known before V can be determined from Figure 7-6.
7-15
-------
I
t-t
O»
Figure 7-6. fffect of vegetative cover on relative emission rate.
-------
The amount of vegetative cover en a single field can DS esce-tainec
by collecting and weighing clean res'Jje from a representative plot sr oy
visual comparison with calibrated onotographs. The weight obtained by
either measuring method oust then be converted to an equivalent weight of
flat small-grain stubble before entering Figure 7-6, since different crop
residues vary 1n their ability to reduce wind erosion. Detailed
descriptions of the Measuring methods or conversion procedures are tso
complex for this presentation. Interested readers are referred to the
USOA for these descriptions.
The residue left on a field when using good soil conservation
practices 1s closely related to the type of crop. Table 7-3 presents
representative values of V for common field crops-when stubble or mulch 1s
left after the crop. These values should be used 1n calculating windblown
dust emissions unless a knowledge of local farming practices indicates
that some Increase or decrease Is warranted. Note that three of the five
variables 1n the windblown dust equation are determined as functions of
the crop grown on the field.
7.1.2.1.3 Summary. The estimated emissions 1n tons/acre/yr nay now
be calculated for each field or group of fields as the product of the five
variables times the constant "a* estimated to be 0.025, and the particle
size multiplier for PH10 estimated to be 0.5.
For regional emission estimates, the acreage 1n agriculture should be
determined for each jurisdiction (e.g.,' county) toy croc. "I" and "C"
values can be determined for Individual jurisdiction, with the remaining
three .variables being quantified as functions of crop type. The emission
calculations are best performed 1n a tabular format such as the one snown
in Table 7-4. The calculated emissions from each crop are summed to get
agricultural wind erosion emissions by jurisdiction and these are totalec
to get emissions for this source category for the entire region.
. 7.1.2.1.4 appropriate Usage of Results. Inherent variabilities in
the many parameters used in the windblown dust equation cause the results
to be less accurate than emission estimates for most other sources.
However, the rough estimates provided by the proposed procedure are better
than not considering this source at all 1n particulate emission Inventory
7-17
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• TABLE 7-4. CALCULATION SHEET FOR ESTIMATION OF DUST FROM MIND EROSION
Juris- I,C. K, L, V, L'. V . E, •» Tot.ll
diction Based on Climatic Surface Field Veget. Length Vegot. alKC- Kmi:isi<>i>:
(County) Soil Typo Factor Crop Acres Roughness Length Cover Factor Factor I.'V* hy rrnii
Alfalfa
Oarlcy
Deans
Corn
Cotton
Potatoes
Sorghum
Soybeans
Sugar
Deets
Vcgets.
Wheat
Etc' _
- • TotnT
_-- - — • _^_^^^^-_J^^-^^-^^^— i .^^—,^^m^tm mt —•,^^__^^^^^^ ^ » -. .
(List of
Crops
Crown in
Juris-
diction)
Tot.il
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work. Inclusion of this source category, possibly with some qualifying
statement as to Its relative accuracy, gives an Indication of Its
contribution to regional air quality.
The estimation procedure 1s not Intended for use In predicting
emissions for short t1«e periods, nor can 1t be used 1n determining
edsslon rates for enforcement purposes.
7.1.2.2 Hew Wind Erosion Prediction Technology. New technology for
prediction of agricultural wind erosion Is currently being developed by
the U.S. Department of Agriculture. This undertaking was recently
described by L. J. Hagen as follows.'
Currently, the U.S. Department of Agriculture 1s taking a
leading role 1n combining erosion science with data bases and
computers to develop what should be a significant advancement
1n wind erosion prediction technology. In 1986 an Initial
group composed of Agricultural Research Service (ARS) and Soil
Conservation Service (SCS) scientists was formed to begin
development of a new Wind Erosion Prediction System (HEPS).
Additional scientists are now being added to the group to
strengthen specific research and technology development
areas. The objective of the project 1s to develop replacement
technology for the Wind Erosion Equation.
• The primary user of wind erosion prediction technology Is
the USDA Soil Conservation Service, which has several major
applications. First, as a part of the periodic National
Resource Inventory, 1t collects data at 300,000 primary
sampling points, and at central locations, calculates the
erosion losses occurring under current land use practices.
The analyzed results are used to aid In developing regional
and national policy.
Second, SCS does conservation planning of wind erosion
control practices to assist farmers and ranchers In meeting
erosion tolerances. Implementation of adequate conservation
plans preserves land productivity and reduces both onsfte and
offslte damages. Conservation planning requires a prediction
system that will operate on a personal computer and produce
answers 1n a relatively short time. In addition, WEPS must
serve as a communication cool between conservation planners
and those who implement the plans.
Various users also undertake project planning in which
erosion prediction 1s used to evaluate erosion and deposition
1n areas Impacted by the project. In this aopHcation, more
time and resources may be expended than in conservation
planning to collect input data and make analyses. Project
7-19
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planning 1s typically carried out by mult1d1sc1pHnary teams
Including field personnel w.^- collect needed Input data.
Other users of wind erosion prediction technology
represent a wide range of problem areas. Often their problems
will require development of additional models to supplement
WEPS 1n order to obtain answers of Interest. Some of these
diverse problem areas Include evaluating new erosion control
techniques, estimating long-term soil productivity changes,
calculating onslte and offslte economic costs of erosion,
finding deposition loading of lakes and streams, computing the
effects of dust on add rain processes, determining Impact of
management strategies on public lands, and estimating
visibility reductions near airports and highways.
From the preceding survey of user needs, 1t 1s apparent
that the prediction technology must deal with a wide range of
soil types and management factors. Wind erosion prediction
technology also must cover a broad range of climatic and
geographic regions 1n the United States. The major Impact of
wind erosion 1s 1n the Great Plains, but credible areas 1n the
Great Lakes region, the semlarld western United States, and
windy coastal regions are all affected.
7.2 DEMONSTRATED CONTROL TECHNIQUES
7.2.1 Tilling
Operational modifications to tilling of the soil Include the use of
novel Implements or the alteration of cultural techniques to eliminate
some operations altogether. All operational modifications will affect
soil preparation or seed planting operations. Furthermore, the suggested
operational modifications are crop specific. Estimated PM10 efficiencies
for agricultural controls are presented In Table 7-5.
The punch planter 1s a novel Implement which might have applications
for emissions reduction from planting cotton, corn, and lettuce. The
punch planter 1s already being used In sugar beet production. The punch
planter punches a hole and places the seed Into It, as opposed to
conventional planters which make a trough and drop the seeds 1n at a
specified spacing. The advantage 1s that punch planters can leave much of
the surface soil and surface crop residues undisturbed. Large-scale use
of the punch planters would require Initial capital Investments by the
farming Industry for new equipment.
7-20
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Attachment 6
Excerpts from Superfund Exposure Assessment Manual (EPA88c)
66
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EP A/540/1 -« 8/001
OSWER Directive 928S.S-1
April 1888
Superfund Exposure Assessment
Manual
U.S. Environmental Protection Agency
Office of Remedial Response
Washington, DC 20460
-------
quantified property values. These data are available
for many chemicals that may be present at
uncontrolled hazardous waste sites, and are found in
various chemical reference texts. In cases where
chemical data are missing, the analyst must estimate
the property values. This section provides equations
for estimating certain requisite chemical properties.
Comprehensive guidance for chemical property
estimation is providdd in reference materials such as
Lyman et al. (1982). Readily accessible computerized
systems are available to predict a range of pertinent
chemical properties. The computerized Graphic
Exposure Modeling System (GEMS), and its
subsystem CHEMEST. is an example. The EPA
Office of Toxic Substances in Washington, O.C. has
developed and is managing this system. Essentially a
computerized version of Lyman et al. (1982), it can
be rapidly accessed to estimate the chemical
characteristics necessary for volatilization estimation.
The user of this manual can refer to Farino et al.
(1983) for a detailed review and evaluation of existing
equations for estimating volatilization from
uncontrolled hazardous waste sites. This report
presents a survey of available air release models for
volatile substances and a critical analysis of the
applications and limitations of each.
(1) Landfills Without Internal Gas Generation
Equation 2-3 can be used to estimate volatile
releases from covered landfills containing toxic
materials alone, or toxic materials segregated from
other landfilled nonhazardous wastes. Equations 2-4
through 2-7 are used to calculate certain input
variables that are required to apply Equation 2*3.
Farmer et al. (1978) developed an equation to
estimate the effectiveness of various landfill cover
types and depths in controlling volatile releases'. This
equation, based .on Pick's First Law of steady state
diffusion, assumes that diffusion into the atmosphere
occurs at a plane surface where concentrations
remain constant It ignores biodegradation, transport
in water, adsorption, and production of landfill gas.
Diffusion of the toxic vapor through the soil cover is
the controlling factor. It also assumes that there is a
sufficient mass of toxicant in the landfill so that
depletion of the contaminant will not reduce the
emission rate.
Equation 2-3, simplified by Farmer et al. (USEPA
1980b), incorporates a number of assumptions (see
Farino et al. 1983 for a complete discussion), such as
completely dry soil (worst case) and zero
• Although computerized dispereion modeing can be uMd to
obtam contaminant release me*, tt is primarily a tool tor
determine contaminant atmospheric fat*. Thus, rater to
Chapter 3. Environmental Fate Anaiyaia. tor detailed
discussions ol a* dispersion models applicable to unoontroaad
hazardous waste tacMea.
concentration of volatilizing material at the soil
surface. Shen (1981) converted Farmer's simplified
equation for calculating the vapor flux rate to a form
that provides a toxic vapor emission rate by
multiplying the basic equation by the exposed
contaminated surface area. In addition. Shen modified
the equation to allow calculation of the volatilization
rate of a specific component of the overall toxic
mixture by multiplying by the weight fraction of the
component in the mixture. However, as pointed out
by Farino et al. (1983), a more accurate approach
would be to multiply by the mole fraction of the toxic
component in the buried mixture. Thus. Farmer's
equation, as modified by Shen (1981) and Farino et
al. (1983). is:*
Ei=Di
(2-3)
where
A
Pt
Mi
•mission rate of component i. (g/sec).
diffusion coefficient of component i in air.
(cm2/sec).
saturation vapor concentration of component
i, (g/cm3).
exposed area. (cm?).
total soil porosity, (dimensionless).
mole fraction of toxic component i in the
wast«,(gmote/gmole).
effective depth of soil cover, (cm).
Note that total soil porosity, rather than air-filled sofl
porosity, is used in this equation. The presence of
water in a soil cover will tend to decrease the flux rate
of a volatile compound by effectively decreasing the
porosity, and also by increasing the geometric
complexity of the sofl pore system (because water
adheres to soil particles), thus effectively increasing
the vapor path (USEPA 1980b). Farmer et al.
suggest however, that when using their equation to
design a landfill cover, the total porosity value be
used (USEPA 1980b), thereby designing for the worst
case (i.e.. dry conditions). In most instances, it wiP be
appropriate to apply this same worst-case logic to
the analysis of volatilization release from landfilled
wastes, assume that landfill cover soils are dry. and
use a value for total porosity in Equation 2-3. It is
recognized, however, that there may be situations
where it can be shown that cover soils exist in a wet
condition more often than in a dry one. In these
cases, the air-filed sol porosity (Pa) may be more
appropriate, and this value can be substituted for Pt
in Equation 2-3 when analyzing volatilization release.
N not providec in existing literature. DI, a compound's
diffusion coefficient (required for the above equation),
can be calculated by Fuller's Method (Perry and
Chilton 1973):
-------
0.001TtTO-
sfe
MW
T
MWt;MWa
P.
ZVi;£Va
(2-4)
absolute temperature. (*K).
.molecular weights of toxic
substance and air (28.8).
respectively, (g/mole).
absolute pressure, (atm).
molecular diffusion volumes of
toxic substance and air (20.1).
This is the sum of the atomic
diffusion volumes of the
compound components.
(cm3/moie).
To estimate short-term (maximum) release rates.
use a value for the temperature that reflects the
expected summer maximum temperatures. Annual
average temperatures should be used to initially
estimate long-term (average) release rates. This
initial estimated long-term release value will be
revised as described in Section 2.3.3 to develop final
long-term release estimates.
Relevant atomic diffusion volumes for use
estimating D{ are (Perry and Chiton 1973):
in
C • 16.5
H - 1.98
0 « 5.48
N « 5.69
Cl
Br
F
S <
19.5
35.0
25.0*
17.0
Aromatic ring • -20.2
Heterocyclic ring • • 20.2
Table 2-3 presents diffusion coefficients that have
been calculated for a variety of compounds, some of
which may be present at abandened sites.
An alternative method (Shen 1981) for approximating
DI involves the identification of a compound listed in
Table 2-3 that has a molecular weight and molecular
diffusion volume (calculated) similar to those of the
toxic substance under evaluation. The unknown
diffusion coefficient can then be calculated using:
0,=]
where
D*
MW,
(2-5)
diffusion coefficient of the compound to
be estimated from the known D'.
diffusion coefficient of a compound that
can be found in the table, the molecular
weight and atomic diffusion, volume of
which are close to that of the unknown.
MW* • molecular weight of the selected
compound D'.
MW( « molecular weight of the compound to
be estimated.
Total sofl porosity,
(USEPA 19806):
can be calculated as follows
(2-6)
where
total soil porosity, (dimensionless).
sod bulk density.* (g/cm3): generally
between 1.0 and 2.0 g/crn3.
particle density, (g/cm3): usually 2.65
g/cm3 used for most mineral material.
For estimation, Pt can be assumed to be
approximately 0.55 for dry, non-compacted soils,
and about 0.35 for compacted soils. This same value
(0.35) is also appropriate for use as a generic air-
filled soil porosity (Pa) when analyzing the
volatilization release from soils with a high moisture
content (Shen 1981). Alternatively, the local Soil
Conservation Service office can be contacted to
obtain she-specific estimated air-filled soil porosity
values for specific locations.
Saturation vapor concentration, C.j, can be
determined by (USEPA 1980b):
(2-7)
where
Ctf » saturation vapor concentration of
component i, (g/cm3).
p • vapor pressure of the chemical," (mm
Hg).
MWj • mole weight of component i, (g/mole).
R » molar gas constant. (62.361 mm Hg-
cm3/mole-*K).
T « absolute temperature. (K).
Again, use maximum summer temperatures to
estimate short-term release and annual average
temperatures to initially estimate long-term release.
•Th» value • from Snan (1881).
• Valuee tor aoi buk danafty for (pacified locations can be
obtained from ttte U.S. Sol Conaervaton Service, 8oM* S Fie
data DAM.
• If th» vapor pretture of • chemicel under consideration ia not
available in standard reference teat, eatimaie it ai deacnoad in
Lyman et al. (1882).
17
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