United States Center for Environmental
Environmental Protection Research Information
Agency Cincinnati, OH 45268
Technology Transfer EPA 762575-877022
xvEPA User's Guide
Emission Control
Technologies and Emission
Factors for Unpaved Road
Fugitive Emissions
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EPA/625/5-87/022
September 1987
User's Guide
Emission Control Technologies
and Emission Factors for
Unpaved Road Fugitive
Emissions
Center for Environmental Research Information
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, OH 45268
Air and Energy Engineering Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Research Triangle Park, NO 27711
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Notice
This document has been reviewed In accordance with the U.S. Environmental Protec-
tion Agency's peer and administrative review policies and approved for publication.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
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Acknowledgments
This handbook is primarily a summary of the results of two independent studies, one
conducted by Midwest Research Institute (MRI), Kansas City, Missouri, and the other
by Southern Research Institute (SORI), Birmingham, Alabama. The MRI Report, Identi-
fication, Assessment and Control of Fugitive Paniculate Emissions was written by Chat-
ten Cowherd Jr., John S. Kinsey and Dennis Wallace; Dale Harmon of EPA's Air and
Energy Engineering Research Laboratory was project officer. The SORI report, Criti-
cal Review of Open Source Paniculate Measurement, Part II - Field Comparison, was
written by Bobby E. Pyle and Joseph McCain; Robert McCrillis of EPA's Air and Energy
Engineering Research Laboratory was project officer.
This handbook was adapted and produced by Marjorie Fitzpatrick, JACA Corporation,
Fort Washington. Pennsylvania. Norman Kulujian of EPA's Center for Environmental
Research Information. Cincinnati, Ohio, was the EPA project officer for this publica-
tion.
If you wish to obtain the more detailed reports, they are available from the National
Technical Information Service (NTIS), 5285 Port Royal Road, Springfield, Virginia
22161; (703) 487-4650. The order numbers are:
MRI Report; PB 86-230083; EPA-600/8-86-023
SORI Report; PB 86-239787; EPA-600/2-86-072
IV
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Contents
1 Introduction 1
1.1 Background 1
1.2 Organization of Document 1
1.3 References 2
2 Overview of Source Testing Methods 3
2.1 Mass Emissions Measurements 3
2.2 Particle Sizing Methods A
2.3 References 8
3 Uncontrolled Fugitive Road Dust Emission Factors 9
3.1 Published Emission Factors 9
3.2 Quality Rating System for AP-42 Emission Factors 9
3.3 Other Types of Emission Factors 12
3.4 Recommendations for Use of Emission Factors 13
3.5 References 13
4 Control Alternatives 15
4.1 Wet Suppression 15
4.2 Chemical Stabilization 15
4.3 Physical Stabilization 16
4.4 Other Unpaved Road Control Techniques 16
4.5 Estimation of Cost-Effectiveness of Control Options 16
4.6 References 18
5 Estimation of Control System Performance 19
5.1 Wet Suppression i9
5.2 Chemical Stabilization 20
5.3 Paving 21
5.4 Other Control Alternatives 21
5.5 Calculation of Controlled Emission Rate 22
5.6 Alternate Indicators of Control Performance 24
5.7 References 24
6 Fugitive Emissions Control Strategy Development 27
6.1 Identifying/Classifying Fugitive Emission Sources 27
6.2 Preparing an Emissions Inventory 27
6.3 Identifying Control Alternatives 29
6.4 Estimating Control Efficiencies 29
6.5 Calculating Cost and Cost-Effectiveness 29
6.6 References 31
Appendices
A Glossary of Terms 33
B Abbreviations 35
C Modeling of Fugitive Emissions 37
D Control Efficiency Decay Curves 39
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Tables
3-1 Typical Silt Contents for Unpaved Road Surface Materials 11
4-1 Implementation Alternatives for Stabilization of an Unpaved Road 17
5-1 Field Data on Watering Control Efficiency 20
5-2 Classification of Tested Chemical Road Dust Suppressants 21
5-3 Summary of Major Unpaved Road Dust Suppressant Control Efficiency Tests 22
5-4 Summary of Major Unpaved Road Dust Suppressant Control Efficiency Decay
Function Tests 23
6-1 Uncontrolled Emission Factors for Hypothetical Crushing Plant 29
6-2 Plant and Process Data for Hypothetical Facility 30
6-3 Source Extents and Uncontrolled Emission Rates for Hypothetical Crushing Plant 30
6-4 Cost Comparison for Two Selected Implementation Scenarios 31
VI
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Figures
2-1 Illustration of upwind/downwind sampling method 3
2-2 Illustration of exposure profiling sampling method 4
2-3 Cyclone/impactor combination 5
2-4 Lundgren cascade impactor 6
2-5 Stacked filter profiler head 6
3-1 Mean number of days with greater than or equal to 0.01 in. of precipitation 10
6-1 Process flow diagram of hypothetical rock crushing plant 28
D-1 Control efficiency decay for an initial application of Petro Tac® 39
D-2 Control efficiency decay for an initial application of Coherex® 40
D~3 Control efficiency decay for a reapplication of Coherex® 41
D-4 TSP control efficiency decay for light-duty traffic on unpaved roads 42
D-5 Control efficiency decay for LiquiDow® applied to haul roads 43
D-6 Control efficiency decay for Soil Sement® and Biocat-Enzyme® applied
to haul roads 44
D-7 Control efficiency decay for Flambinder® applied to haul roads 45
D-8 Control efficiency decay for Arco 2200® applied to haul roads 46
D-9 Control efficiency decay for reapplication of various chemical suppressants 47
VII
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Chapter 1
Introduction
1.1 Background
During the past decade, research has shown that
participate emissions from open sources such as
unpaved roads contribute significantly to ambient
particulate matter concentrations in many areas. (1]
The current EPA emission trading policy, [2] com-
monly called the bubble policy, allows excessive
emissions from one source to be offset by improved
control of another source within the same plant. In
implementing the bubble policy, some plants have
agreed to reduce fugitive dust emissions in lieu of
tighter controls on process emissions. [3]
This document has been prepared to assist control
agency personnel in evaluating unpaved road fugi-
tive emissions control plans and to assist Industry
personnel in the development of effective control
strategies for unpaved roads. This document de-
scribes control techniques for reducing unpaved
road emissions, methods for quantifying or estimat-
ing emissions generation, and provides data for esti-
mating the efficiency of the performance of various
control technologies. Although fugitive particulate
emissions can be reduced by reducing the extent of
the source, this document focuses on the use of
"add-on" controls which do not affect the size or
throughput of the source.
Within this document, fugitive emissions refer to
those air pollutants that enter the atmosphere with-
out passing through a stack or duct designed to di-
rect or control their flow. Terms and abbreviations
for specific types and sizes of particulate matter
cited in this document Include:
TP Total airborne particulate matter.
TSP Total suspended particulate matter, as repre-
sented approximately by particles equal to
or smaller than 30 ^m in aerodynamic diame-
ter. [4,5]
IP Inhalable particulate matter consisting of
particles equal to or smaller than 15 )j,m in
aerodynamic diameter. [4]
PMio ParUculate matter consisting of particles equal
to or smaller than 10 p.m in aerodynamic di-
ameter. [4]
RP Respirable particulate matter consisting of
particles equal to or smaller than approxi-
mately 3.5 nm in aerodynamic diameter. [4]
FP Fine particulate matter consisting of parti-
cles equal to or smaller than 2.5 ^m In aero-
dynamic diameter. [4]
Other terms used In this document are defined In Ap-
pendix A.
1.2 Organization of Document
This document is organized as follows:
Chapter 2 - Source Testing Methods: describes
the mass and particle sizing methods used to
measure unpaved road emissions.
Chapter 3 - Uncontrolled Fugitive Road Dust Emis-
sion Factors: discusses published (AP-42)
emission factors, emission factors developed
for specific sources using linear regression
techniques, and the emission factor rating sys-
tem for published emission factors.
Chapter 4 - Control Alternatives: describes possi-
ble techniques for reducing fugitive road dust
emissions and briefly discusses calculation of
cost effectiveness.
Chapter 5 - Estimation of Control System Perform-
ance: presents available data for estimating
the effectiveness of control techniques.
Chapter 6 - Fugitive Emissions Control Strategy:
presents a fully worked industrial example
(with emphasis on unpaved road emissions) Il-
lustrating the procedural steps for control
strategy development, Including the capital,
operation and maintenance costs of represen-
tative controls.
Appendix A - Glossary of Terms.
Appendix B - Abbreviations: lists and defines ab-
breviations used In this report,
Appendix C - Modeling of Fugitive Emissions: pre-
sents a general discussion of ambient air qua!
Ity modeling.
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Appendix D - Control Efficiency Decay Curves: fig-
ures depicting control efficiency decay
curves.
1.3 References
1. Cowherd, C., Jr. and R.W. Hendriks. Fugitive
Emissions from Integrated Iron and Steel
Plants, Open Dust Sources. Paper 77-6.2,
presented at the 70th Annual Meeting of the
APCA, Toronto, Canada, June 1977.
2. Federal Register. Emissions Trading Policy
Statement, 51 FR 43813-43860, December 4,
1986.
3. Palmisano, J. and D. Martin. "The Use of Non-
traditional Control Strategies in the Iron and
Steel Industry: Air Bubbles, Water Bubbles,
and Multimedia Based Control Strategies." Pa-
per 84-39.1, presented at the 77th Annual
Meeting of the APCA, San Francisco. Califor-
nia, June 1984.
4. Cowherd, C. Jr. and J.S. Kinsey. Identification,
Assessment, and Control of Fugitive Particulate
Emissions. EPA-600/8-86-023, U.S. Environ-
mental Protection Agency, Research Triangle
Park, NC, August 1986.
5. U.S. Environmental Protection Agency Compi-
lation of Air Pollution Emission Factors. Volume
1: Stationary Point and Area Sources Fourth
Edition, Supplement A, AP-42. Office of Air
Quality Planning and Standards. Research Tri-
angle Park. NC, October 1986.
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Chapter 2
Overview of Source Testing Methods
Testing verifies the rates of uncontrolled emissions
from the most significant sources and establishes
the relative importance of each of those sources. In
addition, source testing provides valuable data on
the emission characteristics of each source, which
in turn aids considerably in selecting the most suit-
able control method for each source. This chapter
presents an overview of the testing methods used to
sample emissions from unpaved roads.
Fugitive paniculate emission rates and particle size
distributions are difficult to quantify because of the
diffuse and variable nature of fugitive emission
sources and the wide range of particle sizes In-
volved (including particles which deposit immedi-
ately adjacent to the source). Standard source test-
ing methods, which are designed for testing con-
fined flows under steady-state, forced-flow condi-
tions, are not suitable for measurement of fugitive
emissions.
2.1 Mass Emissions Measurements
Field measurement of fugitive mass emissions from
unpaved roads is usually conducted using either the
upwind/downwind method or the exposure profiling
method. The former involves measurement of up-
wind and downwind particulate concentrations, utiliz-
ing ground-based samplers under known meteoro-
logical conditions, followed by calculation of source
strength (mass emission rate) with atmospheric dis-
persion equations. The exposure profiling method
involves simultaneous, multi-point measurements of
particulate concentration and wind speed over the
effective cross-section of the plume, followed by
calculation of net particulate mass flux through inte-
gration of the plume profiles.
2.1.1 The Upwind/Downwind Method
The basic procedure of the upwind/downwind
method, shown schematically in Figure 2-1, is to
measure particulate concentrations both upwind and
downwind of the pollutant source using ground level
samplers. The required number of upwind sampling
instruments depends on how well the source opera-
tion can be isolated (i.e.. the absence of Interfer-
ences from other sources upwind). At least five
downwind particulate samplers must be operating
during a test; increasing the number of downwind in-
struments will improve the reliability in determining
the emission rate by providing better plume defini-
tion. [1]
Figure 2-1. Illustration of upwind/downwind
sampling method.8
Upwind
Sampler
Downwind k.
J Samplers /
}'
Plume
Centerline
/ Wind
Instrument
A modified drawing from reference 2.
After the concentrations measured upwind are sub-
tracted from the downwind concentrations, the net
downwind concentrations are input to dispersion
equations (normally of the Gaussian type). The dis-
persion equations are used to back-calculate the
particulate emission rate required to generate the
pattern of downwind concentrations. (See Appendix
C for a brief discussion of Gaussian air quality mod-
els and the parameters used in the models). A num-
ber of meteorological parameters must be concur-
rently recorded for input to this dispersion equation.
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At a minimum, the wind direction and speed must be
recorded on-site.
Figure 2-2. Illustration of exposure profiling
sampling method. [3]
2.1.2 The Exposure Profiling Method
This method uses the profiling concept that Is the
basis for conventional (ducted) source testing. The
difference is that in the case of exposure profiling,
the ambient wind directs the plume to the sampling
array. The passage of airborne paniculate matter
immediately downwind of the source Is measured di-
rectly by means of simultaneous multi-point sam-
pling of particulate concentration and wind velocity
over the effective cross section of the fugitive emis-
sions plume. For measurement of fugitive emis-
sions, profiling sampling heads are distributed over a
vertical network positioned just downwind (usually
about 5 m) from the source. Particulate sampling
heads should be symmetrically distributed over the
concentrated portion of the plume containing about
90% of the total mass flux (exposure). A vertical line
grid of at least three samplers is sufficient for meas-
uring emissions from line or moving point sources.
At least one upwind sampler must be operated to
measure background concentration, and wind
speed must be measured concurrently on-site. Fig-
ure 2-2 Illustrates the exposure profiling method.
Unlike the, up wind /downwind method, exposure pro-
filing uses a mass-balance calculation scheme
rather than requiring Indirect calculation through the
application of a generalized atmospheric dispersion
model. The mass of airborne particulate matter
emitted by the source is obtained by spatial integra-
tion of distributed measurements of particulate flux,
after subtracting the background contribution. The
exposure is the point value of the flux (concentra-
tion of airborne particulate accumulated over the
time of measurement).
2.1.3 Recommended Sampling Procedures
The method of choice In measuring fugitive emis-
sions from unpaved roads is the exposure profiling
method. [4,5]. Measurement results of a line source
(such as unpaved road) obtained using the expo-
sure profiling method are more accurate than those
obtained by the upwind/downwind method. [5] The
exposure/profiling method Is source-specific, and
its increased accuracy over the upwind/downwind
method is a result of the fact that emission factor
calculation is based on direct measurement of the
emission rate. [5]
Maximum exposure values from unpaved roads usu-
ally occur at a height of 1.5 to 2.0 meters above
ground level. However, there may be significant
O Profiler Head (See below left)
O Cyclone/lmpactor (See below right)
"A Anemometer
^ Wind Vane O
Profiler Head
with Motor
and Flow
Controller
Cyclone
Preseparator
with 5 Stage
Cascade
Impactor,
dust exposures at heights up to at least 9 meters.
The exposure profile method provides better char-
acterization of the plume generated by vehicular
traffic on an unpaved road because emissions are
measured at multiple heights in the dust plume.
The Ideal exposure profiling system to characterize
unpaved road mass emissions would have mass
samplers situated at 1.5, 2.5, 4,0, 6.0, 7.5 and 10
.meters above the road surface. Each sampling head
would contain a horizontally mounted high-volume
filter and an inlet nozzle below the filter. It is possible
to sample isokinetically at each mass sampler on the
profiler tower if each sampler is equipped with a
servo system and individual velocity sensors. This
sampling set-up provides continuous adjustment of
the flow rate based on wind velocity at each eleva-
tion on the profiler tower.
2.2 Particle Sizing Methods
Three fundamentally different methods are com-
monly used to perform particle size measurements
on open dust emission sources — cyclone/impac-
tors, stacked filters, and scanning electron micros-
copy. These measurement techniques will be dis-
cussed in the following sections. For particle size
analysis of fugitive road dust emissions, sampling Is
generally done in conjunction with total particulate
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sampling using the exposure profiling method dis-
cussed in the previous section.
A fourth type of particle size sampler, the EPA di-
chotomous sampler, is useful for quantifying fine
particulate mass concentrations. This sampler was
designed with a symmetrical size-selective Inlet
(usually having a particle size outpoint of 10 or 15
jxmA) which is insensitive to wind speed or direction.
However, this device operates at a low flow rate (1
m3/hr), yielding only 0.024 mg of sample In 24 hours
for each 10 (ig/m3 of TSP concentration. Thus, an
analytical balance of high precision is required to de-
termine mass concentrations below and above the
fine particulate (2.5 urn) outpoint (the minimum in
the typical bimodal size distribution of atmospheric
particulate).
2.2.1 Cyclone/lmpactors
High-volume cascade impactors with glass fiber Im-
paction substrates, which are commonly used to
measure mass size distribution of atmospheric par-
ticulate, may be adapted for sizing fugitive particu-
late emissions. A cyclone preseparator (or other
device) precedes the Impactor to remove coarse
particles which otherwise would be subject to parti-
cle bounce within the impactor, causing fine particle
bias. The set-up of a cascade Impactor with a cy-
clone preseparator is shown in Figure 2-3, The
analysis of the particulate matter collected by this
device is by gravimetric methods. Multiple sampling
devices are used to obtain a representative sample
- one upwind and two or more downwind using
either the upwind/downwind or profiling method dis-
cussed previously. The sampling intake should be
pointed into the wind and the sampling velocity ad-
justed to the mean local wind speed by fitting the
intake nozzle of appropriate size to obtain a sample
at or near isokinetic conditions.
Figure 2-4 shows a second type of cascade impac-
tor, the Lundgren impactor. These impactors are
four stage (plus filter) inertial classifiers. [6,7,8] The
samples are taken quasi-isoklnetlcally with Inlet noz-
zle cross sections adjusted in accordance with the
mean wind speed at the start of the test. Particle
collection in this impactor takes place on a moving
plastic film coated with an adhesive. The adhesive is
used to insure retention of the Impacted particles.
The particle size distribution is measured gravimetri-
cally. By collecting the particles on the moving film,
overlapping of particles In the collected sample Is
largely avoided, permitting individual particles col-
lected on each stage to be easily examined by mi-
croscope.
Figure 2-3. Cyclone /impactor combination.
05
Scale-Inches
Cyclone
5 Stage Cascade
Impactor
Back-up Filter
Holder
2.2.2 Stacked Filters
A schematic diagram of a stacked filter sampler Is
shown in Figure 2-5, A stainless steel inlet screen
having ;a 30 \i.m pore size Is used to provide a nomi-
nal 30 |im fractionatlon point. The screen Is followed
by two Nuclepore filters, the first of which has a
pore size (8 pm) selected to provide a 2.5 p.m aero-
dynamic diameter cutoff while the second, which
has a pore size of 0.8 urn, serves as the final filter.
2.2.3 Microscopy
Another particle sizing technique gaining some re-
cent prominence Is microscopy. Microscopes used
in particle sizing Include optical or light micro-
scopes, transmission electron microscopes (TEM),
and scanning electron microscopes (SEM). SEM is
the most widely used microscopy technique for de-
termining particle size distributions.
Using the SEM technique, selected portions of the
filters from the collection devices are removed,
placed between finely perforated stainless steel
screens, and backwashed with acetone or back-
blown with compressed air to remove the collected
particles. For samples collected using the acetone
backwash procedure the acetone particle suspen-
sion is then agitated and the particles are
redeposlted by vacuum filtration onto 0.2 urn pore
size Nuclepore filters. Air-suspended particles from
the compressed air backblown procedure are recol-
lected on similar Nuclepore filters for analysis. A thin
coating of carbon is evaporated onto the
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Figure 2-4. Lundgren cascade Impactor.
1st Stage Nozzle
1st Stage Rotating Plastic Film (with Adhesive)
Air Inlet
redeposited specimen after mounting on SEM sam-
ple stubs.
Of the many techniques available to size particles by
their physical dimensions as observed through the
microscope, the most common approach is the
projected area technique. Particle volumes are esti-
mated by categorizing the particle shapes and esti-
mating overall dimensions based on the measure-
ments and assigned shape category. X-ray fluores-
cence emissions are used to assign a probable
composition class to each particle. A density Is then
Figure 2-5. Stacked filter profiler head.
assigned to the particle. After measuring approxi-
mately 700 to 4,500 particles and assigning esti-
mated volumes and densities, a physical diameter-
weight distribution curve is constructed. Aerody-
namic diameters are also estimated for each particle
based on the assigned category, the measured di-
mensions, and aerodynamic shape factors. The
choice of particle measuring technique and the
method for deciding that a sufficient number of parti-
cles have been measured varies among firms per-
forming the analysis.
Bracket
to Tower
Stainless Steel
Inlet Screen
Nuclepore Filters
1,0 In I.D.
Inlet
Orifice
400
Mesh
Screen
Front
Filter
Holder
Back
Filter
Holder
Adapter
Section
\
V,
Hivo1 '
T__
V
/ X
/ /\.
Electrical I / X
Line
V
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Because this work requires several tedious hours to
perform manually, automated processes have
arisen. An example is the use of automatic image
analysis for optical computer controlled scanning
electron microscopy (CCSEM).
2.2.4 Comparison of Particle Sizing Methods
There are advantages and disadvantages with each
of the particle sizing techniques described in the
previous section.
Particle sizing is performed at only two stages with
the stacked filter technique (2.5 and 30 urn), which
means that all other particle sizes (such as the com-
mon 10 p,m size) are inferred based on the two siz-
ing points. Another problem with this method is that
the 30 pm screen, the first sizing outpoint in the
sampler, becomes effectively smaller during sam-
pling due to impaction and interception of smaller
particles on the screen. This reduces the effective
cutoff diameter of the screen to a value smaller than
30 jam, which could cause systematically low results
for all particle sizes.
The procedures used to prepare samples for SEM
analysis could cause bias among both the large and
small particles. The filter retention screens used in
transferring the sample from the profiler filter (the
original collection medium) to the Nuclepore mate-
rial will themselves act as filters and bias the sample
taken from the profiler filter by suppressing the
transfer of large particles. At the other end of the
size spectrum, it is unlikely that the techniques used
to remove particles from the profiler filters would be
effective in removing smaller particles. Thus the bulk
of the small particles removed would be in the form
of agglomerates which would be counted as larger
particles. In order to properly characterize the size
distribution, measurements of about 4,500 particles
are needed to obtain a statistically valid sample; this
is generally larger than the number of particles cur-
rently counted for SEM analysis. There are also in-
herent difficulties in estimating particle volumes and
assigning aerodynamic diameters to the irregular,
nonhomogeneous particles encountered in fugitive
road dust.
current 20 cfma flow rate. A second potential prob-
lem is errors resulting from the possible transfer of
material from the outlet of the cyclone tube to the
first stage of the impactor.
2.2.5 Recommended Particle Sizing Method
There are long recognized problems in reconstitut-
ing size distributions of airborne particles from
resuspenslons of bulk material. For fugitive emission
sampling of unpaved roads the sizing should take
place prior to collection (or concurrent with collec-
tion as in cyclones and Impactors). The recom-
mended procedure for measuring the particle size
distribution is the cyclone/impactor technique. [4]
The ideal particle sizing sampling technique for fugi-
tive road dust emissions is composed of profiler
towers (upwind and downwind), with cyclone/im-
pactors placed at 1.5, 4.5, and 7.0 meters above
the road surface on the tower. Sizing is conducted
using a cyclone for the removal of large particles
followed by a high-volume cascade Impactor for
size distribution measurement. The size distribution
measurements are made separately from the expo-
sure measurement.
With regard to field operations, reduction in the
sampling flow rate from the commonly used 20 cfm
to 15 cfm would help minimize errors from particle
bounce. Alternatively, adhesive coated substrates
could be used at'the current 20 cfm flow rate. Errors
resulting from the possible transfer of material from
the outlet tube of the cyclone to the first stage of
the impactor can be avoided by counting only the
material collected in the body of the cyclone as its
catch. The outlet tube catch would then be com-
bined with that of the first impactor stage.
With respect to particle sizing data analysis, the
technique commonly used in reducing impactor data
from industrial sources is appropriate. [9,10,11] A
spline fit is made to the cyclone/impactor data in the
cumulative percentage form of the distribution. The
fit is made in a manner that requires continuity In the
slope of the curve, and the solution is forced to be
asymptotic to 100% at a diameter equal to the maxi-
mum diameter present in the sample. The fitted
curve is then used to interpolate or extrapolate as
needed to obtain the mass fractions in the selected
size intervals. This technique avoids the assumption
of a functional form for the distribution and makes
The cyclone/impactor method obtains data directly
on an aerodynamic basis at five to seven cutpoints.
One problem with the technique Is potential particle
bounce in the sampler. Particle bounce can be
overcome by sampling at a reduced flow rate (15
cfm) or by using adhesive coated substrates at the
' 20 cfm is a specific sampling flow rate used by Sierra high
volume cascade impactors (Model No. 230) designed to
sample fugitive road dust. Theoretically, sampling flow
rates can vary by manufacturer, although there are no
other known manufacturers of cascade impactors used in
this application.
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use of the complete data set rather than just two of
the data points.
2.3 References
1. Cowherd, C.. Jr. and J.S. Klnsey. Identifica-
tion, Assessment, and Control of Fugitive Par-
tlculate Emissions. EPA-600/8-86-023, U.S.
Environmental Protection Agency, Research
Triangle Park, NC, August 1986.
2. Axetell, K., Jr. and C. Cowherd, Jr. Improved
Emission Factors for Fugitive Dust from West-
ern Surface Coal Mining Sources, Volumes I
and II. EPA-600/7-84-048 (NTIS PB84
-170802), U.S. Environmental Protection
Agency, Research Triangle Park. NC, March
1984.
3. Cowherd, C., Jr., et al. Development of Emis-
sions Factors for Fugitive Dust Sources.
EPA-450/3-74-037 (NTIS PB-238 262/0), U.S.
Environmental Protection Agency, Research
Triangle Park, NC, June 1974.
4. Pyle, Bobby E. and J.D. McCain. Critical Re-
view of Open Source Partlculate Emission
Measurements, Part II - Field Comparison.
EPA-600/2-86-072, U.S. Environmental Pro-
tection Agency, Research Triangle, NC, 1986.
5. TRC Environmental Consultants, Inc. Protocol
for the Measurement of Inhalable Particle Fugi-
tive Emissions for Stationary Industrial
Sources. EPA Contract No. 68-02-3115, Task
114, U.S. Environmental Protection Agency,
Research Triangle Park, NC, March 1980.
6. Lundgren, D.A. "An Aerosol Sample for Deter-
mination of Particle Concentration as a Func-
tion of Size and Time," Journal APCA, 17
(4):225-259, 1967.
7. Rao, A.K. Sampling and Analysis of Atmos-
pheric Aerosols., Particle Technology Labora-
tory Publication No. 269, University of Minne-
sota. 1975.
8. Wesolowskl, J.J., A.E. Alcocer, and B.R. Ap-
pel. "The Validation of the Lundgren Impac-
tor," In: The Character and Origins of Smog
Aerosols, (9): 125-146, 1980.
9. Johnson, J.W.. Q.I. Clinard, L.G. Felix, and
J.D. McCain. A Computer-Based Cascade Im-
pactor Data Reduction System.
EPA-600/7-78-042 (NTIS PB-285 433), U.S.
Environmental Protection Agency, Research
Triangle Park, NC, 1978.
10. Tatsch, C.E., W.M. Yeager. and G.L.
Johnson. Interactive Particle Data Reduction
for Fine Particle Testing. Paper presented at
the Third Symposium on Advances in Particle
Sampling and Measurement, U.S. Environ-
mental Protection Agency, Research Triangle
Park, NC. 1981.
11. TRS-80 Cascade Impactor Data Reduction
System. Denver Research Institute, P.O. Box
10127, Denver, CO 80208.
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Chapter 3
Uncontrolled Fugitive Road Dust Emission Factors
The large number of individual sources and the di-
versity of source types make impractical the field
measurement of emissions at each point of release.
In most cases the only feasible method for determin-
ing source-by-source emissions is to estimate the
typical emission rate for each type of source and to
adjust each estimate for the size or activity of the
specific source and the level of control. Emission
rates can be estimated using published emission
factors (AP-42) or emission factors developed dur-
ing in-house sampling. An emission factor Is an esti-
mate of the quantity of emissions released to the
atmosphere relative to appropriate units of weight,
volume, distance or duration of the activity that
emits the pollutant. For unpaved roads, emission
factors are expressed in pounds per vehicle mile
traveled (or kilograms per vehicle kilometer trav-
eled).
3.1 Published Emission Factors
The document Compilation of Air Pollutant Emission
Factors (commonly called AP-42), published by the
EPA since 1972, is a compilation of emission factor
reports for the most significant emission source
categories. Data obtained from source tests, mate-
rial balance studies, and engineering estimates are
used to calculate the emission factors in AP-42.
The predictive equation for unpaved road emissions
described in AP-42, expressed in pounds of particu-
late per vehicle mile traveled (Ib/VMT) or kilograms
per vehicle kilometer traveled (kg/VKT), Is as fol-
lows:a
.12 A 30
(for Ib/VMT)
All abbreviations used in this report are defined in
Appendix B as well as in the text where the abbre-
viation first appears.
0.7
0.5
(for kg/VKT)
where
E =
k =
s =
S =
W =
emission factor, Ib/VMT (kg/VKT)
particle size multiplier, dlmensionless
silt content of road surface material, %
passing a 200 mesh (75 urn) screen
mean vehicle speed, mph (km/hr)
mean vehicle weight, tons (Mg, metric
tons)
w = mean number of wheels, dimensionless
p = number of days with at least 0.01 In.
(0.254 mm) of precipitation per year,
dimensionless.
The particle size multiplier, k, varies with the aero-
dynamic particle size range. The values for the parti-
cle size multiplier k, as listed In the AP-42 docu-
ment, are: 0.80 (<30 um), 0.50 (<15 urn), 0.36
(<10 jxm), 0.20 (<5 urn), and 0.095 (<2.5 (Jim).
The silt content of the road surface material, s, var-
ies on a site-by-site basis. Silt content Is deter-
mined using the ASTM-C-136 method; however, the
Information on Table 3-1 can be used to approxi-
mate the silt content for unpaved roads.
Figure 3-1 shows the mean number of days with at
least 0.01 inches (0.254 mm) of precipitation per
year, which is p in equations 3-1 a and b.
3.2 Quality Rating System for AP-42
Emission Factors
Data used to calculate AP-42 emission factors are
obtained from a variety of sources, Including pub-
lished technical papers and reports, documented
emission testing results, and personal communica-
tions. Some data sources provide complete details
-------
Figure 3-1. Mean number of days with greater than or equal to 0.01 in. of precipitation. [1]
,150
VJ .-V-
'xvU4
LOUISIANA1,
(
120
210
240
0 100 200 300 400 500
I 1 i i i i i l i i i
Miles
140
-------
about their collection and analysis procedures,
whereas others provide only sketchy information. A
rating system for AP-42 emission factors was devel-
oped by the U.S. EPA. Office of Air Quality Planning
and Standards to help users assess the reliability and
accuracy of emission factors. The system entails 1)
rating individual test data sets for accuracy and reli-
ability and 2) calculating an overall rating for the
emission factor based on ratings assigned to individ-
ual data sets.
Several subjective schemes have been used to as-
sign emission factor ratings. Because of the subjec-
tive nature of the rating system, a rating should only
be considered an indicator of the accuracy and pre-
cision of a given factor.
The AP-42 rating system is briefly discussed in this
section because the system is used to rate test data
presented in Chapter 5. For a more detailed descrip-
tion of the AP-42 rating system and how it relates to
unpaved road emission factors, other references
are available. [1.2,3]
The rating system for an emission factor test data
set, as it relates to unpaved roads, is based on the
following data standards:
• A - Tests performed by a sound methodology
and reported in enough detail for adequate
validation. These tests are not necessarily EPA
reference method tests, although such refer-
ence methods are certainly to be used as a
guide.
• B - Tests that are performed by a generally
sound methodology but lack enough detail for
adequate validation.
• C - Tests that are based on an untested or new
methodology or that lack a significant amount
of background data.
• D - Tests that are based on a generally unac-
ceptable method but may provide an or-
der-of-magnltude value for the source.
In the ideal situation, a large number of A-rated
source test data sets representing a cross section
of the industry are reduced to a single value for each
individual source by computing the arithmetic mean
of each test set. The emission factor Is then com-
puted by calculating the arithmetic mean of the indi-
vidual source values. Alternatively, regression
analysis Is used to derive a predictive emission fac-
tor equation for the entire A-rated test set. No B-,
C-, or D-rated test sets are used in the calculation
of the emission factor because the number of
A-rated tests is sufficient. This Ideal method of cal-
culating an emission factor is not always possible
because of a lack of A-rated data.
If the number of A-rated tests Is so limited that inclu-
sion of B-rated tests would Improve the emission
factor, then B-rated test data are Included in the
compilation of the arithmetic mean. No C- or
D-rated test data are averaged with A- or B-rated
test data. The rationale for inclusion of any B-rated
test data Is in the background Information document.
If no A- or B-rated test series are available, the
emission factor is the arithmetic mean of the C- and
D-rated test data. The C- and D-rated test data are
used only as a last resort, to provide an or-
der-of-magnitude value.
In AP-42, the reliability of these emission factors Is
indicated by an overall Emission Factor Rating rang-
ing from A (excellent) to E (poor). These ratings
Table 3-1. Typical Silt Contents for Unpaved Road Surface Materials"
Industry
Copper smelting
Iron and steel production
Sand and gravel processing
Stone quarrying and processing
Taconite mining and processing
Western surface coal mining
Rural roads
Road Use or
Surface Material
Plant road
Plant road
Plant road
Plant road
Haul road
Service road
Access road
Haul road
Scraper road
Haul road (freshly
graded)
Gravel
Dirt
Crushed limestone
Plant
Sites
1
9
1
1
1
1
2
3
3
2
1
2
2
Test
Samples
3
20
3
5
12
8
2
21
10
5
1
5
8
Silt (%
Range
15.9- 19.1
4.0 - 16.0
4.1 -6.0
10.5-15.6
3.7 - 9.7
2.4-7.1
4.9 - 5.3
2.8 - 18.0
7.2 - 25.0
18.0 - 29.0
NA
5.8-68.0
7.7-13.0
)
Mean
17.0
8.0
4.8
14.1
5.8
4.3
5.1
8.4
17.0
24.0
5.0
28.5
9.6
"Reference 1.
''Expressed on a weight per weight basis.
11
-------
take into account the type and amount of data from
which the factors were calculated, as follows:
• A - Excellent. Developed only from A-rated
data taken from many randomly chosen facili-
ties In the industry population. The source
category is specific enough to minimize vari-
ability within the source category population.
• B - Above average. Developed only from
A-rated data from a reasonable number of fa-
cilities. Although no specific bias is evident,
the facilities may not represent a random sam-
ple of the Industry. As in the A rating, the
source category is specific enough to mini-
mize variability within the source category
population.
• C - Average. Developed only from A- and
B-rated data from a reasonable number of fa-
cilities. Although no specific bias is evident,
the facilities tested may not represent a ran-
dom sample of the industry. As in the A rating,
the source category is specific enough to
minimize variability within the source category
population.
• D - Below average. Developed only from A-
and B-rated data from a small number of facili-
ties, and these facilities may not represent a
random sample of the industry. There also may
be evidence of variability within the source
category population. Limitations on the use of
the emission factor should be footnoted.
• E - Poor. Developed from C- and D-rated
data, and the facilities tested may not repre-
sent a random sample of the industry. There
may be evidence of variability within the
source category population. Limitations on the
use of these factors are always footnoted.
The equations listed in AP-42 (equations 3-1 a and b
discussed previously) to calculate an emission fac-
tor for unpaved roads have an Emission Factor Rat-
ing of A. The A rating for size-specific emission fac-
tors is based on two criteria. First, the test data
were developed using well documented and sound
methodologies. Second, a total of at least six tests
were performed at two or more plant sites. [3]
However, the A Emission Factor Rating is only appli-
cable with all the following conditions:
• Road surface silt content between 4.3 and
20%
• Mean vehicle weight between 3 and 157 tons
(2.7-147 Mg)
• Mean vehicle speed between 13 and 40 mph
(21-64 km/hr)
• Mean number of wheels between 4 and 13.
Also, the A rating only applies if site specific data
(such as silt content) is available. The rating is low-
ered to a B if assumptions are made for site-specific
data.
The rating system discussed in this section will be
used to rate test data presented in Chapter 5 of this
report.
3.3 Other Types of Emission Factors
The most reliable emission factors are based on field
tests of representative sources using a sound test
methodology reported in enough detail for adequate
validation. Usually the emission factor for a given
source operation, as presented in a test report, is
derived simply as the arithmetic average of the indi-
vidual emission factors calculated from each test of
that source. Frequently the range of individual emis-
sion factor values is also presented.
As an alternative to the presentation of an emission
factor as a single-value arithmetic mean, an emis-
sion factor may be presented in the form of a pre-
dictive equation derived by linear regression analy-
sis of test data. The predictive emission factor
equation mathematically relates emissions to
parameters which characterize source conditions.
An emission factor equation is useful if it is success-
ful in explaining much of the observed variance in
emission factor values on the basis of correspond-
ing variances in specific source parameters. This
enables more reliable estimates of source emissions
on a site-specific basis by allowing for correction of
the emission factor to specific source conditions.
Examples of site-specific predictive emission factor
equations developed using linear regression tech-
niques are as follows:
E = k (W) (m)
(3-2)
where
E =
m =
W =
k.a.b
emission factor, Ib/VMT (kg/VKT)
road surface moisture content, %
mean vehicle weight, tons (Mg)
constants dependent upon the size
fraction of the particulate being con-
sidered
12
-------
and
able predictor of unpaved road emissions currently
available.
0 139 -0 203 0 267 0 395
= k(s) (R) (w) (W)
where
p
R =
W =
w =
(3-3)
emission factor, Ib/VMT (kg/VKT)
ambient relative humididty, %
mean vehicle weight, tons (Mg)
mean number of wheels, dimensionless
s = silt content of road surface material
k = particle size multiplier, dimensionless
Equations 3-2 and 3-3 are provided only as samples
of equations developed using linear regression tech-
niques. The equations were derived from a set of
data from a specific site and the equations are, most
likely, only applicable for the specific site. Also note
that the equations do not take into account the
amount of rainfall.
3.4 Recommendations for Use of Emission
Factors
The utility of an emission factor predictive equation
is that of predicting the emissions from a particular
site in lieu of actual measurements. In order that the
equation be applicable over a wide range of site lo-
cations and conditions, it should include as many of
the relevant parameters describing the site as pos-
sible. This requires that the predictive equation be
developed from as large a data base as possible.
The equation currently described in the AP-42 man-
ual was developed from a fairly broad data base us-
ing multiple linear regression techniques. The AP-42
emission factor equation is probably the most reli-
The high emission factor rating for the AP-42 equa-
tion for calculating emissions from unpaved roads is
only applicable within specific limits of road surface
silt content and mean vehicle speed, weight, and
number of wheels (see Section 3.2). If actual plant
conditions stray outside the limits of the AP-42
equation, it may be necessary to conduct mass or
particle size testing to determine emission rates.
AP-42 emission factors are useful for estimating
emissions from an air pollutant source. However,
the factors are averages obtained from a wide range
of data with varying degrees of accuracy. Emissions
calculated using AP-42 emission factors are likely to
be different from a facility's actual emissions. For
the most accurate estimate of emissions, site-spe-
cific data should be obtained whenever possible.
3.5 References
1. U.S. Environmental Protection Agency. Compi-
lation of Air Pollution Emission Factors, Volume
1: Stationary Point and Area Sources. Fourth
Edition, Supplement A, AP-42, Office of Air
Quality Planning and Standards, Research Tri-
angle Park, NC, October, 1986.
2. Cowherd, C., Jr. and B. Peterman. AP-42 Up-
date, Section 11.2, Fugitive Dust Sources.
EPA-450/4-83-010. U.S. Environmental Pro-
tection Agency, Research Triangle Park, NC.
March 1983.
3. Cowherd, C., Jr. and P.J. Englehart. Size Spe-
cific Particulate Emission Factors for Industrial
and Rural Roads — Source Category Report.
EPA-600/7-85-051. U.S. Environmental Pro-
tection Agency, Research Triangle Park, NC,
October 1985.
-------
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Chapter 4
Control Alternatives
There are several options for control of fugitive par-
tlculate emissions from unpaved roads. Emission re-
duction may be achieved by reducing the source
extent (the level of the source activity) or by using
measures to prevent or reduce emission genera-
tion.
Although the reduction of source extent results in a
highly predictable reduction in the uncontrolled
emission rate, such an approach usually requires a
change in the process operation. Frequently, reduc-
tion in the extent of one source may necessitate the
Increase in the extent of another, as in the shifting of
vehicle traffic from an unpaved road to a paved
road, The option of reducing source extent is be-
yond the scope of this manual and will not be dis-
cussed further.
Wet suppression, chemical stabilization, and physi-
cal stabilization are feasible control techniques for
unpaved road fugitive particulate emissions. This
chapter describes the basic characteristics of each
control technique and briefly discusses cost effec-
tiveness.
In selecting or evaluating a control alternative, it is
important to keep in mind the overall environmental
impact of the control alternative. For example, a
chemical suppressant used to stabilize the road sur-
face could cause ground water or surface water
problems If the chemical is improperly applied. In
other words, to reduce an air pollution problem, a
water pollution problem was created.
4.1 Wet Suppression
Wet suppression systems for unpaved roads apply
either water or a water solution of a chemical agent
to the surface of the road. Application of chemical
agents in a water solution will be addressed in the
next section of this chapter. Wet suppression pre-
vents or suppresses the fine particles contained in
that material from leaving the surface and becoming
airborne. The suppressant agglomerates and binds
the fines to the aggregate surface, thus eliminating
or reducing its emissions potential.
Plain water has been used as a wet suppression sys-
tem for many years on such sources as crushing,
screening, and materials transfer operations as well
as unpaved roads. Water Is generally applied to the
surface of unpaved roads by a truck or some other
vehicle using either a pressurized or a gravity flow
system. Watering unpaved roads is only a tempo-
rary measure and must be repeated at regular inter-
vals.
Wet suppression with plain water can cause freezing
problems in the winter. In the arid West, wet sup-
pression is not always practical due to inadequate
water supplies.
To improve the overall control efficiency of wet dust
suppression systems, wetting agents can be added
to the water to reduce the surface tension. The ad-
ditives allow particles to more easily penetrate the
water droplet and increase the number of droplets,
thus increasing the surface area and contact poten-
tial.
4.2 Chemical Stabilization
Particulate release from unpaved surfaces can be
reduced or prevented by stabilizing those surfaces.
The use of chemical dust suppressants for stabiliza-
tion has received much attention in the past several
years. Chemical suppressants can be classified into
six generic categories: salts (i.e.. CaCI2 and MgCI2).
lignin sulfonate, wetting agents, latexes, plastics,
and petroleum derivatives.
Salts, which are usually obtained from natural brine
deposits, control dust by absorbing and retaining
moisture in the surface material. Wetting agents
lower the surface tension of water, thereby causing
more rapid penetration into the surface material.
The remaining dust suppressants, both natural and
synthetic, bind the fines to larger aggregates in the
surface material.
Chemical dust suppressants are generally applied to
the road surface as a water solution of the agent.
The degree of control achieved is a direct function
of the application Intensity, dilution ratio, and fre-
quency (number of applications/unit time) of the
chemical applied to the surface. Control depends or
the type and number of vehicles using the road.
15
-------
4.3 Physical Stabilization
Physical stabilization techniques can also be used
for the control of fugitive emissions from unpaved
road surfaces. Physical stabilization includes any
measure, such as compaction of fill material at con-
struction sites, which physically reduces the emis-
sions potential of a source from either mechanical
disturbance or wind erosion.
The most notable form of physical stabilization of
current interest involves the use of civil engineering
fabrics or "road carpet" for unpaved roads. In prac-
tice, the road carpet fabric Is laid on top of a prop-
erly prepared road base just below a layer of coarse
aggregate (ballast) The fabric sets up a physical
barrier that prevents the fines (particles less than 75
urn in diameter) from contaminating the ballast
layer. These smaller particles are now no longer
available for resuspension and saltation resulting
from the separation of the fines from the ballast. The
fabric is also effective in distributing the concen-
trated stress from heavy-wheeled traffic over a
wider area. Currently, this application has limited ac-
tual use; therefore detailed information on the effec-
tiveness of this technique is not yet available.
4.4 Other Unpaved Road Control
Techniques
Other practices may be used to reduce fugitive par-
ticulate emissions from an open dust source. Work
practices focus on transport equipment operation.
For an unpaved travel surface, emissions can be re-
duced by decreasing vehicle speed and weight.
Housekeeping practices generally refer to the peri-
odic removal of exposed dust-producing materials
to reduce the potential for dust generation through
wind or machinery action. Examples of housekeep-
ing measures that may be used to reduce unpaved
road emissions include clean-up of spillage on travel
surfaces or elimination of mud/dirt carryout onto
roads at construction and demolition sites. Any such
housekeeping method can be employed depending
on the source, its operation, and the type of dust-
producing material involved.
A recent study indicates that paved road cleaning
techniques (such as flushing and vacuuming) may
be used to increase the control efficiency of chemi-
cally treated unpaved roads. [1] This preliminary
conclusion was drawn based on one test series con-
ducted on an unpaved road in an iron and steel
plant
4.5 Estimation of Cost-Effectiveness of
Control Options
Development and evaluation of particulate fugitive
emissions control strategies require analysis of the
relative costs of alternative control options. The pri-
mary goal of any cost analysis Is to provide a con-
sistent comparison of the real costs of alternative
control measures.
The general approach for cost analysis is to calcu-
late the cost-effectiveness for each proposed con-
trol alternative. Cost-effectiveness is the ratio of the
annualized cost of the emissions control to the
amount of emissions reduction achieved.
Calculation of cost-effectiveness for comparison of
control measures or strategies can be accom-
plished in four steps. First, the alternative control/
cost scenarios are selected. Second, the capital
costs of each scenario are calculated. Third, the an-
nualized costs for each of the alternatives are devel-
oped. Finally, the cost-effectiveness is calculated
as a ratio, taking into consideration the level of emis-
sions reduction.
The first step in the cost analysis is to select a set of
specific control/cost scenarios from the general
techniques. The specific scenarios will include defi-
nition of the major cost elements and identification
of specific implementation alternatives for each of
the major cost elements. For unpaved road control
techniques these major cost elements include capi-
tal equipment elements and operation/ maintenance
elements. For example, the major cost elements for
chemical stabilization of an unpaved road Include: a)
chemical acquisition; b) chemical storage; c) road
preparation; d) mixing the chemical with water; and
e) application of the chemical solution. The first step
in any cost analysis is definition of these major cost
elements.
For each major cost element, several implementa-
tion alternatives can be chosen. Options within each
cost element include such choices as buying or
renting equipment; shipping chemicals by rallcar,
truck tanker, or in drums via truck; alternative
sources of power or other utilities; and use of plant
personnel or contractors for construction and main-
tenance. The major cost elements and the imple-
mentation alternatives for each of these elements
for the chemical stabilization example described
above are outlined in Table 4-1.
The second step is to calculate capital costs for
each scenario. The capital costs of a fugitive emis-
sions control system are those direct and indirect
expenses incurred up to the date when the control
system is placed in operation. These capital costs
include actual purchase expenses for control equip-
ment, labor and utility costs associated with installa-
tion of the control system, and system startup and
shakedown costs. In general, direct capital costs
are the costs of control equipment and the labor,
material, and utilities needed to install the equip-
ment. Indirect costs are overall costs to the facility
16
-------
Table 4-1. Implementation Alternatives for Stabilization of
an Unpaved Road
Cost Elements
Implementation Alternatives
Purchase and ship
chemical
Store chemical
Prepare road
Mix chemical and water
in application truck
Apply chemical solution
via surface spraying
Sliip in railcar tanker (11,000-22,000
gal/tanker)
Ship in truck tanker (4,000-6,000 gal/
tanker)
Ship in drums via truck (55 gal/drum)
Store on plant property
— In new storage tank
-In existing storage tank
Needs refurbishing
Needs no refurbishing
— In railcar tanker
Own railcar
Pay demurrage
— In truck tanker
Own truck
Pay demurrage
— In drums
Store in contractor tanks
Use plant-owned grader to minimize ruts
and low spots
Rent contractor grader
Perform no road preparation
Put chemical in spray truck
— Pump chemical from storage tank or
drums into application truck
— Pour chemical from drums into appli-
cation truck, generally using forklift
Put water in application truck
— Pump from river or lake
— Take from city water line
Use plant-owned application truck
Rent contractor application truck
Incurred by the system but not directly attributable
to specific equipment items. Indirect costs include
engineering/design, construction/field expenses,
contractor's fee, shakedown/startup and contin-
gency costs.
The third step in calculating cost-effectiveness is to
calculate annuallzed costs for the control scenario.
The annualized cost of a fugitive emission control
system includes operating costs such as labor, ma-
terials, utilities, and maintenance items as well as
the annuallzed cost of the capital equipment. The
annualization of capital costs is a classical engineer-
ing economics problem, the solution of which takes
Into account the fact that money has time value.
These annualized costs are dependent on the inter-
est rate paid on borrowed money or collectable by
the plant as Interest (if available capital Is used), the
useful life of the equipment and depreciation rates of
the equipment.
The annualized costs of control equipment can be
calculated from:
Ca=CRF (Cp) + C0 + 0.5C0
(4-1;
where:
Ca = annualized costs of control equipment
($/year)
CRF = Capital Recovery Factor
0 = installed capital costs ($)
Co = direct operating costs ($/year)
0,5 = plant overhead factor
The capital recovery factor (CRF) combines interest
on borrowed funds and depreciation into a single
factor. It is a function of the interest rate and the
overall life of the capital equipment and can be esti-
mated by:
CRF =•
K1+D
(1+D -1
(4-2)
where:
I = Interest rate (annual % as a fraction)
n = economic life of the control system
(years)
The final step In calculating cost-effectiveness is to
calculate the actual cost-effectiveness ratio. This
ratio is defined as:
C* =•
A R
(4-3)
where:
C* = cost-effectiveness ($/mass of emissions
reduced)
C9 = annualized cost of control equipment
($/year)
AR = annual reduction in particulate emissions
(mass/year)
The annual reduction in particulate emissions can be
calculated from the following equation:
A R = MEc
(4-4)
17
-------
where:
M = annual source extent (i.e., measure of
source size or level of activity — such as
vehicle miles traveled per year)
E = uncontrolled emission factor (i.e., mass of
uncontrolled emissions per unit of source
extent)
c = average control efficiency expressed as a
fraction
Collection of the data to conduct a cost analysis can
sometimes be difficult. If a well defined system Is
being costed, the best sources of accurate capital
costs are vendor estimates. However, if the system
is not sufficiently defined to develop vendor esti-
mates, published cost data can be used. Published
sources of cost data for fugitive emission control
systems are included in references 2 through 7. Ref-
erences 2 through 4 relate primarily to open dust
control systems while references 5 through 7 can
be used to estimate component costs for both open
dust and process fugitive emissions control sys-
tems.
Often, published cost estimates are based on differ-
ent time-valued dollars. These estimates must be
adjusted for inflation so that they reflect the most
probable capital investments for a current time and
can be consistently compared. Capital cost Indices
are the techniques used for updating costs. These
indices provide a general method for updating over-
all costs without having to complete in-depth stud-
ies of individual cost elements. Indices typically
used for updating control system costs are the
Chemical Engineering Plant Cost Index, the Bureau
of Labor Statistics Metal Fabrication Index, and the
Commerce Department Monthly Labor Review,
4.6 References
1. Muleskl, G.E. and C. Cowherd, Jr. Evaluation
of the Effectiveness of Chemical Dust Sup-
pressants on Unpaved Roads. U.S. Environ-
mental Protection Agency, Research Triangle
Park, NC, Draft Final Report, May 1986.
2. Cusclno, T., Jr. Cost Estimates for Selected
Dust Controls Applied to Unpaved and Paved
Roads in Iron and Steel Plants. EPA Contract
No. 68-01-6314, Task 17, U.S. Environmental
Protection Agency, Region V, Chicago, IL, April
1984.
3. Cuscino, T,, Jr., G. E. Muleski, and C. Cow-
herd, Jr. Iron and Steel Plant Open Source Fu-
gitive Emission Control Evaluation. EPA-
600/2-83-110, U.S. Environmental Protection
Agency, Research Triangle Park, NC, October
1983.
4. Muleski, G. E., T. Cusclno, Jr., and C. Cow-
herd, Jr. Extended Evaluation of Unpaved Road
Dust Suppressants In the Iron and Steel Indus-
try. EPA-600/2-84-027 (NTIS PB 84-110568),
U.S. Environmental Protection Agency, Re-
search Triangle Park, NC, February 1984.
5. Neveril, R.V, Capital and Operating Costs of
Selected Air Pollution Control Systems.
EPA-450/5-80-002 (NTIS PB 84-154350).
GARD, Inc., December 1978.
6. Richardson Engineering Services, Inc. The
Richardson Rapid Construction Cost Estimating
System: Volume I - Process Plant Construction
Estimating Standards. 1983-84 Edition.
7. Robert Snow Means Company, Inc. Building
Construction Cost Data. 1979.
-------
Chapter 5
Estimation Of Control System Performance
The principal control measures for unpaved roads
are wet suppression, chemical stabilization, and
paving. This chapter will discuss available perform-
ance data and design considerations for each of
these control measures. Other control approaches,
such as physical stabilization, will be discussed
briefly. Work practices, such as speed control on
unpaved travel surfaces, will not be discussed.
Performance capabilities of unpaved road dust con-
trols can be affected by four categories of vari-
ables: a) control application parameters; b) vehicle
characteristics; c) properties of the surface to be
treated; and d) climatic factors. Furthermore, be-
cause of site-to-site differences in most of these
parameters, the performance of a given control
system can be expected to vary significantly from
one application to another. Therefore, in using the
control efficiency data presented in this section,
care must be taken to document the source and
control parameters tied to each control efficiency
data set. The selection of a control technique in-
volves the evaluation of both performance charac-
teristics and cost considerations. No Individual table
or figure can provide all the required information.
Most of the control techniques involve periodic
rather than continuous control application, for exam-
ple, watering unpaved travel surfaces. The control
efficiency is cyclic, peaking immediately after appli-
cation, then eroding with time. Because of the finite
durability of these control techniques, ranging from
hours to months, it is essential to relate an average
efficiency value to a frequency of application. For
measures of extended durability such as paving, the
application program required to sustain control ef-
fectiveness should be indicated. One common pitfall
to be avoided is using field data collected soon after
control measure application to represent the aver-
age control efficiency over the lifetime of the meas-
ure.
For a periodically applied control measure, the most
representative value of control efficiency is the time
average, given by:
C(T) =
1 r1
T 0J
c(t) dt
(5-11
where:
cm
c(t)
average control efficiency during pe-
riod of T days between application (%)
instantaneous control efficiency at t
days after application (%), where
t
-------
t = time between applications, hr
The data to support this empirically based mathe-
matical model are shown In Table 5-1 along with ad-
ditional results from testing of unpaved haul roads
with water control. No significant difference In the
average control efficiency of watering as a function
of particle size has been established to date. As with
all empirical models, equation 5-2 should not be ap-
plied beyond the ranges of independent variable val-
ues tested.
5.2 Chemical Stabilization
5.2.1 Design Considerations
The control application parameters affecting control
performance of chemical dust suppressants are: a)
application intensity; b) application frequency; c)
dilution ratio; and d) application procedure. Applica-
tion intensity is the volume of diluted solution applied
per unit area of surface (for example, l/m2 or
gal/yd2). The higher the intensity, the higher the an-
ticipated control efficiency. However, this relation-
ship applies only to a point, because too Intense an
application will begin to run off the surface.
Application frequency is the number of applications
per unit of time. The dilution ratio is the volume of
chemical concentrate to the volume of water (for
example, a 1:7 dilution ratio = 1 part chemical to 7
parts water).
The decay in control efficiency of a chemical dust
suppressant occurs largely because vehicles travel-
ing over the road surface impart energy to the
treated surface which breaks the adhesive bonds
that keep fine particles on the surface from becom-
ing airborne. An increase in vehicle weight and
speed accelerates the decay in efficiency for
chemical treatment of unpaved roads.
Any action which contributes to the breaking of a
surface crust will adversely affect the control effi-
ciency. For example, the structural characteristics
of an unpaved road affect the performance of
chemical controls. These characteristics are: a)
combined subgrade and base bearing strength, as
measured by the California Bearing Ratio (CBR); b)
amount of fine material (silt and clay) on the surface
of the road; and c) the friability of the road surface
material. Low bearing strength causes the road to
flex and rut in spots with the passage of heavy
trucks; this destroys the compacted surface en-
hanced by the chemical treatment. A minimum
amount of fine material in the wearing surface Is
needed to provide the chemical binder with the par-
ticle surface area necessary for effective interpar-
ticle bonding. Finally, the larger particles of a friable
wearing surface material simply break up under the
weight of the vehicles and cover the treated road
with layer of untreated dust.
Adverse weather usually accelerates the decay of
control performance. For example, freeze-thaw cy-
cles break up the crust formed by chemical binding
agents; heavy precipitation washes away water-sol-
uble chemical treatments like llgnln sulfonates; and
Intense solar radiation dries out watered surfaces.
On the other hand, light precipitation might improve
the efficiency of water extenders and hygroscopic
chemicals like calcium chloride.
Table 5-1. Field Data on Watering Control Efficiency
Location
N. Dakota
New Mexico
Ohio
Missouri
Mine 1
Mine 1
Mine 1
Mine 2
Mine 2
Reference(s)
2-4
2-4
2-4
2-4
7
7
7
7
7
No. of
Tests
4
5
3
2
—
—
—
—
_
Month
October
July /Aug.
November
September
_
_
_
_
_
Application
Intensity
(L/m2)
0.2
0.2
0.6
1.9
—
_
_
_
_
Average Time
Between
Applications
-------
5.2.2 Performance Data
The control of dust emissions from unpaved roads
has received the widest attention in the literature
(see Table 5-2). Exposure profiling and upwind/
downwind sampling have been used to measure
control efficiencies for watering and for a range of
chemicals which bind the surface material or In-
crease its capacity for moisture retention. Tables
5-3 and 5-4 summarize the measured performance
data for chemical dust suppressants.
The observed control efficiency decay functions for
several dust suppressants are shown In a series of
nine figures contained in Appendix D (Figures D-1
through D-9). Most of the data on the figures are
expressed in terms of vehicle passes rather than
time because vehicle traffic Is the primary cause of
the loss of control effectiveness. The control effi-
ciency decay functions can be used to derive the
critical relationships between average control effi-
ciency and application frequency. Assuming, as a
first approximation, that control efficiency decays
linearly from an Initial value of 100%, the average
control efficiency for a given frequency of applica-
tion is twice the value at the end of the decay cycle.
The quality rating of control performance data for a
periodically applied control measure must address
the reliability of the average control efficiency for
the particular application frequency tested. Obvi-
ously, a spread In the measured values of Instanta-
neous control efficiency is expected as the effi-
ciency decays. The quality rating must be based on
how well the Instantaneous values fit a decay func-
tion. At the time of this writing, mathematically de-
rived decay functions were available for only a few
of the control measures. Therefore, no quality rat-
Ings were assigned to the control efficiency data
presented.
Table 5-2. Classification of Tested Chemical Road Dust
Suppressants
Dust
Suppressant
Category
Petroleum-based
Lignosulfonates
Salts
Polymers
Surfactants
Mixtures
Trade Name
Petro Tac®
Coherex®
Arco 2200®
Arco 2400®
Generic 2 (QSI*
Lignosite
Trex®
Peladow®
LiquiDow®
Dustgard®
Oil Well Brine
Soil Sement®
Biocat®
Arcote 220® /
Flambinder*
Number of
Valid
Controlled
Tests"
13
130
20
91
8
73
3
1
34
11 (17)
4
32
3
4
Reference
Numbers
2-4,6
2-4,6,8-10
7
11
6
11
12
13
7
11
8
6,7
7
8
"Numbers without parentheses represent total suspended particulate
(TSP) and numbers in parentheses represent respirable particulate
(RP).
'This is a petroleum resin product developed at the Mellon Institute for
the American Iron and Steel Institute.
5.3 Paving
The control efficiencies afforded by paving unpaved
road segments can be estimated by comparing the
AP-42 emission factors for the unpaved and paved
road conditions. The emission factor for the paved
road condition requires an estimated silt loading on
the paved surface. An urban street dust loading
model[14] can be used to estimate silt loadings as a
function of traffic volume. The model Is expressed
as follows:
In most of the extended tests of control perform-
ance, efficiency values were found to decay with
vehicle passes (and time) after application. In Fig-
ures D-1 through D-3 and D-9. the best-fit linear
decay functions determined by least-squares analy-
sis are shown. In Figures D-4 through D~8, the data
points are connected by line segments.
Apparent Increases In control efficiency with vehicle
passes were observed in several test series from
Reference 7. This behavior Is thought to be the re-
sult of moisture effects on the uncontrolled emission
rate, which was measured simultaneously with each
controlled emission rate. In other words the
efficiency values were not always referenced to a
dry uncontrolled emission rate.
sL = 21,3 (ADT)"
(5-3)
where:
sL = silt loading, oz/yd2 (g/m2)
ADT = average daily traffic, vehicles/day
This urban model was developed from silt loading
measurements in five urban areas (Baltimore; Buf-
falo; Granite City, IL; Kansas City; and St. Louis). All
of the streets were paved edge to edge and had
curbs and gutters. The calculated control efficien-
cies for paving are usually on the order of 90%.
5.4 Other Control Alternatives
A number of open source control techniques have
not yet been quantitatively evaluated for control effi-
21
-------
Table 5-3. Summary of Major Unpaved Road Dust Suppressant Control Efficiency Tests
Ref.
No.
2-4
9-10
11
12
Dust
Suppressant
Tested
Coherex®
Coherex®
Coherex®
Coherex®
Coherex®
Coherex®
Arco 2400®
Lignosite
(50% solids)
Dustgard®
Peladow®
Trex®
(ammonium
lignin
sulfonate)
No. of
Valid
Controlled
Tests
2
4
5
4
2
91
91
73
11 (17)*
1
3
Test
Site
Steel plant
Steel plant
Steel plant
Steel plant
Steel plant
Public road
Public road
Public road
Public road
Surface coal
mine
Taconite mine
Measurement
Method"
P
P
P
P
P
U/D
U/D
U/D
U/D
P
P
Days
After
Application
<7
1-2
1-2
Unknown
14-15
30-270
30-270
30-270
3-60
90
<7
Application
Intensity
(gal sol/
yd2)
Unknown
0.19
0.19
Unknown
Unknownd
l.S'/0.33f
3.5
0.125V0.25/
0.5
0.6
0.08
Dilution
Ratio
(gal chem:
gal H2O)
1:9
1:6
1:6
Unknown
1:4-1:7
\:5'/-[:9f
1:0
M'/l:!/
1:0*
1:2
1:4
Average
Vehicle
Weight
1ST)
3
50
3
4-19
26
4
4
4
4
3
110-127
Control
Efficiency*
(%)
91C
TP: 92-98
TSP: 91-96
FP: 90-97
TP: 94-100
TSP: 91-99
FP: 92-97
TP:81
TP:99
TSP: 53
RP: 64
TSP: 96
RP:57
TSP: 46
RP:42
TSP: 48
RP: 24
TSP: 95
RP:95
FP.-88
TSP: 88
"P = profiling; U/D = upwind/downwind.
*TP = total paniculate; TSP = total suspended paniculate; RP = respirable paniculate; FP = fine paniculate.
cParticles of less than 30^/m stokes diameter (47)jm aerodynamic diameter).
dFour applications; testing began 2 weeks after fourth application.
"Initial application.
^Repeat application.
6Eleven TSP tests and 17 RP tests conducted.
^Dilution as shipped unknown; no further dilution.
clency. These methods include physical stabilization
of unpaved surfaces, mud/dirt carryout control, and
vegetative stabilization. Vegetative stabilization can
be used only when the material to be stabilized Is
inactive and will remain so for an extended time pe-
riod; therefore, the technique has limited, if any, ap-
plication to controlling unpaved road emissions. Ref-
erences which describe these control alternative
methods in further detail are available in the litera-
ture. [15-20]
5.5 Calculation of Controlled Emission
Rate
Calculation of the estimated emission rate for a
given source requires data on source extent, un-
controlled emission factor, and control efficiency.
The mathematical expression for this calculation Is
as follows:
= ME (1 -c)
(5-4)
where:
R = estimated controlled mass emission rate
M = source extent
E = uncontrolled emission factor, i.e., mass of
uncontrolled emissions per unit of source
extent
c = fractional efficiency of control
The source extent is the appropriate measure of
source size or level of activity which Is used to scale
the uncontrolled emission factor to the particular
source In question. For unpaved roads, the source
extent Is reported In vehicle miles traveled per year
(VMT/yr) or vehicle kilometers traveled per year
(VKT/yr). Source extent Is calculated by multiplying
the average daily traffic count (ADT) by the length of
22
-------
Table 5-4. Summary of Major Unpaved Road Dust Suppressant Control Efficiency Decay Function Tests
Dust
Ref. Suppressant
No. Tested
2-4 Petro Tac®
Coherex®
Coherex®
8 Coherex®
Oil well brine
Arcote
220® / Flam-
binder® Mixture
7 LiquiDow®
Soil Sement®
Biocat®
Flambinder®
Arco 2200®
6 Petro Tac»
Coherex®
Generic 2 (QS)
Soil Sement®
No. of
Valid
Controlled
Tests
8
8
4
5
5
5
8
18
8
12
12
3
4
16
8
16
4
5
6
8
8
Test Site
Steel plant
Steel plant
Steel plant
Steel plant
Steel plant
Steel plant
Surface coal mine 1
Surface coal mine 2
Surface coal mine 3
Surface coal mine 1
Surface coal mine 2
Surface coal mine 3
Surface coal mine 1
Surface coal mine 2
Surface coal mine 3
Surface coal mine 2
Surface coal mine 3
Steel plant
Steel Plant
Steel Plant
Steel Plant
Measurement
Method"
P
P
P
U/D
U/D
U/D
P
P
P
P
P
P
P
P
P
P
P
P
P
P
P
Days
After
Application
2-116
7-41
4-35
17-35
17-35
17-35
14-49
7-28
14-21
21-42
7-35
7-14
14
7-28
7-21
7-28
7
13-30*
13-30*
13-30*
13-30*
Application
Intensity
(gal sol/
yd2)
0.70
0.83C
1.0d'e
1.5
3.8
1.9
0.27-0.6
0.27-0.6
0.3-0.6
1.9-3.0
1.0
2.0
0.5-2.1
0.5-2.0
1.8
0.9-2.8
1.1-2.3
0.21*/0.35'
0.21*/0.36'
0. 14" / 0.46'
0. 16" / 0.44'
Dilution
Ratio
(gal chem:
gal H20)
1:4
1:4C
1:8d.e
1:4
Neat
1:4
1:1.6
1:1.6
1:1.9
1:8.3
1:6.4
1:20,000
1:4.6
1:4.6
1:4.6
1:7
1:6.1
5:1
5:1
5:1
5:1
Average
Vehicle
Weight
(ST)
23-34
27-50
31-56
3
3
3
28-66'
44-83'
70-276'
22-89/
38-82'
70-276'
16-65'
51-69/
70-276'
18-80'
70-276'
9.7-24
9.6-24
9.3-24
9.6-24
Efficiency
Decay
Function
Figure6
D-1
D-2
D-3
D-4
D-4
D-4
D-5
D-5
D-5
D-6
D-6
D-6
D-7
D-7
D-7
D-8
D-8
D-9
D-9
D-9
D-9
°P = profiling; U/D = upwind/downwind.
'Figures are contained in Appendix D.
'Initial application.
^Repeat application.
"Retreated 44 days after the initial application.
A/alues represent range of haul truck weights from empty to loaded vehicles.
Haul truck has 10 wheels at mines 1 and 2 and six wheels at mine 3.
*Days after second application.
"First application.
'Second application, 40 days after first application.
the unpaved road. Each vehicle has a disturbance
width equal to the width of a traffic lane.
The uncontrolled emission factor is calculated by us-
ing predictive equations (see Chapter 3) or meas-
ured using either the upwind/downwind or exposure
profiling methods (see Chapter 2). Normally, the un-
controlled emission factor incorporates the effects
of natural mitigation (such as rainfall), although
emission factors developed as a result of testing un-
der specific conditions may or may not account for
natural mitigation.
Fractional control efficiency can be determined,
through a testing program (before and after testing
of the application of the treatment scheme) or
through estimates available In the literature based on
tests performed under similar conditions (see Fig-
ures D-1 through D-9 in Appendix D and Tables 5-1
through 5-4). There is also a simple model dis-
cussed in this chapter (see equation 5-2) which can
be used to estimate control efficiency for water sup-
pression.
The equation to estimate controlled mass emission
rate is a fairly simple mathematical expression. The
emission rate is dependent on only three variables
— the source extent, the uncontrolled emission fac-
tor, and the fractional control efficiency. Care
should be used in selection or estimation of these
three data variables. A high or low estimate of any of
the variables could yield a misrepresentative value
for the estimated controlled mass emission rate. For
example, an inflated uncontrolled emission factor
and an unusually high fractional control efficiency
could show a greater Improvement In ambient air
quality than actually will be realized. As with any
mathematical expression, the final result is only as
good as the data used in the equation.
23
-------
5.6 Alternate Indicators of Control
Performance
Actual field measurement of controlled and uncon-
trolled emissions from unpaved roads is expensive
and time consuming. Studies have been conducted
in an attempt to develop an alternate method to
monitor the effectiveness of dust control programs.
Early preliminary studies showed a strong correla-
tion between the efficiency of a control technique
and the silt content of surface material. [2,6,21]
Later long-term studies indicated no significant cor-
relation between silt content and control
performance. [3,6] The studies completed to date
used various types of chemical dust suppressants to
control emissions.
Another recent study indicates that the AP-42 emis-
sion factor equation for industrial paved road emis-
sions can be used to conservatively overestimate
unpaved road emissions controlled by chemical dust
suppressants. [6] The paved road emission factor
equation tends to overestimate emissions by a fac-
tor of 1.5 to 2 when applied to situations of typical
chemical suppressant application intensities over
the first 30 days. [6] The emission factor equation
for industrial paved roads is discussed in detail in ref-
erence 22.
5.7 References
1. Letter to Laxmi Kesari, U.S. Environmental
Protection Agency, Washington, D.C., from
Chatten Cowherd, MR), regarding control effi-
ciency achievable by watering, October 21,
1982.
2. Cuscino. T.. Jr., G.E. Muleski, and C. Cow-
herd, Jr. Iron and Steel Plant Open Source Fu-
gitive Emission Control Evaluation.
EPA-600/2-83--110. U.S. Environmental Pro-
tection Agency. Research Triangle Park. NC,
October 1983.
3. Muleski. G.E.. T. Cuscino, Jr., and C. Cow-
herd, Jr. Extended Evaluation of Unpaved Road
Dust Suppressants in the Iron and Steel Indus-
try. EPA-600/2-84--027, U.S. Environmental
Protection Agency, Research Triangle Park,
NC, February 1984.
4. Cowherd. C., Jr., R. Bohn, and T. Cuscino, Jr.
Iron and Steel Plant Open Source Fugitive
Emission Evaluation. EPA-600/2-79-103 (NTIS
PB-299 385), U.S. Environmental Protection
Agency, Research Triangle Park, NC, May
1979.
5. U.S. Department of Commerce, Environmental
Services Administration. Climatic Atlas of the
United States. June 1968, reprinted by the Na-
tional Oceanic and Atmospheric Administration
in 1983.
6. Muleski, G.E. and C. Cowherd, Jr. Evaluation
of the Effectiveness of Chemical Dust Sup-
pressants on Unpaved Roads. U.S. Environ-
mental Protection Agency, Research Triangle
Park, NC, Draft Final Report, May 1986.
7. Rosbury, K. D., and R. A. Zimmer. Cost-Effec-
tiveness of Dust Controls Used on Unpaved
Haul Roads - Volume 1 of 2. Draft Final Report,
U.S. Bureau of Mines, Minneapolis, MN, De-
cember 1983.
8. Russell, D. and S. C. Caruso. A Study of
Cost-Effective Chemical Dust Suppressants
for Use on Unpaved Roads in the Iron and Steel
Industry, American Iron and Steel Institute, De-
cember 1982.
9. Energy Impact Associates. An Alternative
Emission Reduction Option for Shenango Incor-
porated Coke and Iron Works, January 1981.
10. Roffman, A., et al. A Study of Controlling Fugi-
tive Dust Emissions for Nontraditional Sources
at the United States Steel Corporation Facilities
in Allegheny County, Pennsylvania. Report pre-
pared for U.S. Steel Corporation, Pittsburgh,
PA. December 1981.
11. Schanche, G. W., M. J. Savoie, J. E. Davis, V.
Scarpetta, and P. Weggel. Unpaved Road Dust
Control Study (Ft. Carson, CO). Draft Final Re-
port for U.S. Army Construction Engineering
Research Laboratory, Champaign. IL, October
1981.
12. Cuscino, T., Jr. Taconite Mining Fugitive Emis-
sions Study. Minnesota Pollution Control
Agency, Roseville, MN, June 1979.
13. Axetell. K. J. and C. Cowherd, Jr. Improved
Emission Factors for Fugitive Dust from West-
ern Surface Coal Mining Sources - Volumes I
and II. EPA-600/7-84-048 (NTIS PB
84-170802), U.S. Environmental Protection
Agency, Cincinnati, OH, March 1984.
14. Cowherd, C., Jr. and P.J. Englehart. Paved
Road Paniculate Emissions. EPA-600/7-84
-077 (NTISPB84-223734), U.S. Environmental
Protection Agency, Research Triangle Park,
NC, July 1984.
24
-------
15. Kinsey, J.S., et al. A Review of Traditional and
Nontraditional Techniques for the Control of Fu-
gitive Particulate Emissions. Paper No.
80-20.4, 73rd Annual Meeting of the Air Pollu-
tion Control Association. Montreal, Quebec,
June 22-27, 1980.
16. Chepil, N.S., and N.P. Woodruff. "The Physics
of Wind Erosion and Its Control." in Advances
in Agronomy. Vol. 15, Academic Press. NY,
1963.
17. Bonn, R., et al. Dust Control for Haul Roads.
Contract No. J0285015, U.S. Bureau of Mines,
Washington, D.C.. February 1981.
18. Albrecht, S.C.. and E.R. Thompson. Impact of
Surface Mining on Soli Compaction in the Mid-
western U.S.A. Contract J0208016, U.S. Bu-
reau of Mines, Minneapolis, MN. February
1982.
19. Donovan, R.P., et al. Vegetative Stabilization
of Mineral Waste Heaps. EPA-600/2-76-087
(NTIS PB-252 176), U.S. Environmental Pro-
tection Agency, Research Triangle Park, NC,
April 1976.
20. Englehart, P.. and J. Kinsey. Study of Con-
struction Related Mud/Dirt Carryout. EPA Con-
tract No. 68-02-3177, Work Assignment 21.
U.S. Environmental Protection Agency. Region
V. Chicago, IL. July 1983.
21. Cuscino. T., Jr., G.E. Muleski, and C. Cow-
herd, Jr. Determination of the Decay in Control
Efficiency of Chemical Dust Suppressants on
Unpaved Roads. In: Proceedings of the Sym-
posium on Iron and Steel Pollution Abatement
Technology, Pittsburgh. PA. November 16-18,
1982. EPA-600/9-83-016, U.S. Environmental
Protection Agency, Research Triangle Park.
NC, April 1983.
22. U.S. Environmental Protection Agency. Compi-
lation of Air Pollution Emission Factors, Volume
1: Stationary Point and Area Sources. Fourth
Edition, Supplement A, AP-42. Office of Air
Quality Planning and Standards, Research Tri-
angle Park, NC, October 1986.
25
-------
-------
Chapter 6
Fugitive Emissions Control Strategy Development
Developing a fugitive emissions control strategy for
an Industrial facility can be accomplished through a
five step process:
Step 1: Identify and classify all fugitive sources.
Step 2: Prepare an emissions inventory.
Step 3: Identify control alternatives.
Step 4: Estimate control system performance.
Step 5: Estimate control costs and cost-effec-
tiveness.
This section will illustrate these five steps for a hypo-
thetical rock crushing plant with a rated capacity of
300 ton/hr and an actual operating rate of 150
ton/hr. As shown in Figure 6-1, the facility Includes a
primary, secondary, and tertiary crusher; associ-
ated materials sizing, handling, and storage facili-
ties; a paved road; and an unpaved haul road. The
following subsections describe the control strategy
evaluation for this facility. Emphasis will be placed
on calculating emissions, developing a control
scheme, and calculating control costs for unpaved
roads, as this is the primary thrust of this report.
6.1 Identifying/Classifying Fugitive
Emission Sources
The fugitive paniculate emission sources for this fa-
cility, identified schematically in Figure 6-1, include:
• A primary crusher
• A secondary crusher
• A tertiary crusher
• Two screens
• A truck dump station
• Six conveyor transfer points
• Vehicular traffic on unpaved haul road between
the quarry and the plant
• Windblown emissions from product storage
• A front-end loader for loadout of customer
trucks
• Vehicular traffic on a paved road between the
loadout area and the property line.
6.2 Preparing an Emissions Inventory
Calculation of the estimated emission rate for a
given source requires data on source extent, un-
controlled emission factor, and control efficiency.
The mathematical expression for this calculation Is
as follows:
R-ME (1-c>
(6-1;
where
R = estimated mass emission rate
M = source extent
E = uncontrolled emission factor (I.e., mass of
uncontrolled emissions per unit of source
extent)
c = fractional efficiency of control
For this plant we assume that the Initial control effi-
ciency for all sources Is 0%. The uncontrolled emis-
sion factors for the five open dust sources, the 11
process sources, and the source extents are shown
in Table 6-1.
Plant and process data for the hypothetical crushing
plant used to calculate the emission factors are
shown in Table 6-2.
The uncontrolled emission factor for unpaved roads
as presented in Reference 1 Is:
where
E =
k =
s =
S =
(6-2)
emission factor, Ib/VMT
particle size multiplier (dlmenslonless)
silt content of road surface material (%)
mean vehicle speed (mph)
27
-------
Figure 6-1. Process flow diagram of hypothetical rock crushing plant.
Dump Truck
KEY-. \ Indicates fugitive emission point
Y/////////////////////////7/
W = mean vehicle weight (tons)
w = mean number of wheels
p = number of days with at least 0.01 In. of
precipitation per year
Plant data required to calculate the emission factor
are silt content, vehicle speed, mean vehicle
weight, and mean number of wheels. These are
taken from the hypothetical plant data presented in
Table 6-2.
Using the particle size multiplier for TSP and precipi-
tation frequency from Reference 1, the resultant
emission factor for the haul road is:
- 8.86 Ib/VMT
where
k = 0.80 for particles < 30 pimA (see Refer
ence 1)
s = 7.3% (given in Table 6-2)
S = 20 mph (given in Table 6-2)
W = 40 tons (given in Table 6-2)
w = 6 (given in Table 6-2)
p = 140 (see Reference 1, as applied to lower
Great Lakes)
The information in Tables 6-1 and 6-2 can be used
to calculate the source extent for the unpaved haul
road as follows:
days vehicles miles
M = 240 —— X100-—: x 6.3—rr-r
yr day vehicle
= 151,200 VMT/yr
The data on source extent and emission factors can
be substituted into equation 6-1 to obtain the follow-
ing uncontrolled estimated mass emission rate for
the unpaved haul road:
R= 151,200
ton
yr
VMT 2000 Ib
x (1-0)
- 670 ton/yr
Table 6-3 summarizes source extents and uncon-
trolled emission rates for the unpaved haul road as
well as the other particulate emitting sources for the
hypothetical crushing plant.
28
-------
Table 6-1. Uncontrolled Emission Factors for Hypothetical Crushing Plant
Source Emission Factor Equation"'*
Unpaved haul road
Emission Factor
/ s \ /S\ /WX07 /W\05 /365-p\
k(5-9) UJ (lo) (T) (-r) bar)
8.86lb/VMT
Truck dump and
Front end loader
Storage pile erosion
W00018)
W0.0018) -
MY\
IT; Uj
17 f M /365-p\ /f N
1'7 U5J iTarV ClBV
Dump - 0.00019 Ib/ton
Loader-0.000529 Ib/ton
3.2 Ib/acre/day
Paved roads
(v)
- )
0.398 Ib/VMT
Primary crushing
Secondary crushing
Tertiary crushing
Screening
Conveyor transfer
0.28 Ib/ton0
0.28 Ib/ ton"
1.85lb/ton"
0.16/ton/screen"
0.0034 Ib/ton/transfer point"
"Reference 1.
b Refer to Table 6-2 for description of variables used in equations.
6.3 Identifying Control Alternatives
Based on the emissions inventory for the hypotheti-
cal facility, the primary focus of control should be
vehicular traffic on the unpaved haul road with sec-
ondary emphasis on certain process fugitive
sources (primary, secondary, and tertiary crushing,
and screening operations).
Three methods can be used to control emissions
from unpaved roads — wet suppression, chemical
stabilization, and physical stabilization. For this hy-
pothetical facility, chemical stabilization was se-
lected as the most feasible means. Wet suppression
was rejected because of the difficulty in maintaining
watering systems over relatively long stretches of
roads in rural areas. Chemical rather than physical
stabilization was selected because of the temporary
nature of the facility.
The two principal means of controlling emissions
from crushing and screening operations are wet
suppression and capture hoods with an associated
air pollution control device. Wet suppression was
selected as the preferred control because of diffi-
culties associated with the operation and mainte-
nance of capture/collection systems on mobile
crushed stone facilities.
6.4 Estimating Control Efficiencies
A petroleum-based resin, Coherex®, was selected
for chemical dust suppression on the unpaved
road.9 The data in Table 5-3 and Appendix D sug-
gest that an average control efficiency of 90% can
be achieved for up to about 5,000 vehicle passes.
Only limited test data are available on the effective-
ness of wet suppression systems In controlling
emissions from minerals processing operations.
Based on these limited data, the control efficiency
estimates are: [2,3]
Primary crusher: 80%
Secondary crusher: 65%
Tertiary crusher: 50%
Screens: 50%
6.5 Calculating Cost and Cost
Effectiveness
The procedure for calculating the estimated cost
and the associated cost effectiveness of controlling
vehicular emissions by chemical stabilization of the
Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
29
-------
Table 6-2. Plant and Process Data for Hypothetical Facility Table 6-3. Source Extents and Uncontrolled Emission Rates
For Hypothetical Crushing Plant
Process Operation
Operating rate = 150ton/hr
Operating hours = 1,920hr/yr
No. of days with at least 0.01 in. rain (p) = 140°
Haul Road
Average daily traffic = 100 vehicles/day*
Average vehicle weight (W) = 40 tonsc
Average number of vehicle wheels (w) = 6
Average capacity =16 yd3
Average vehicle speed (S) = 20 mph
Roadway length = 6.3 miles
Roadway width = 30 ft
Roadway silt content (s) = 7.3%
Particle size multipler (k) = 0.8 (for particles < 30ymA)"
Truck Dump
Material silt content (s) = 0.5%
Mean wind speed (U) = 5 mph
Drop height IH) = 10 ft
Material moisture content (M) = 2%
Average capacity (Y) = 16 yd3
Particle size multiplier (k) = 0.73 (for particles <30^mA)a
Storage Piles
Storage pile silt content (s) = 2.2%
Storage pile size = 0.5 acre
% time unobstructed wind speed > 12 mph (f) = 20%
Front End Loader
Aggregate silt content (s) = 1.6%
Mean wind speed (U) = 5 mph
Drop height (H) = 5 ft
Aggregate moisture content (M) = 2%
Loader dumping capacity (Y) = 3 yd3
Particle size multiplier (k) = 0.73 (for particles <30^mA)B
Customer Traffic
Road augmentation factor (I) = 1°
No. of travel lanes (n) = 2
Surface silt content (s) = 6%
Surface dust loading (L) = 1,000 Ib/mile
Average vehicle weight (W) = 30 tons'*
Roadway length = 0.5 miles
Average daily traffic = 120 vehicles/day'
Particle size multiplier (k) = 0.86 (for particles < 30^mA)°
"Reference 1.
*50 round trips per day.
'Tare + load - 2 = 28 + 24/2 = 40 tons.
dTare + load + 2 = 20 + 20/2 = 30 tons.
"60 round trips per day.
unpaved haul road at the hypothetical plant Is as fol-
lows:
Step 1 - Determine the Times Between Applications
and the Application Intensity. The following ap-
plication parameters are taken from Table 5-4
and Figures D-2 and D-3 in Appendix D.
Initial application intensity = 0.83 gal of 20%
solution/yd2
Source
Unpaved haul road
Truck dump
Storage pile erosion
Front end loader
Paved roads
Primary crushing
Secondary crushing
Tertiary crushing
Screening
Conveyor transfer points
Source Extent
151,200 VMT/yr
288,000 ton/yr
182 acre day/yr
288,000 ton/yr
14,400 VMT/yr
288,000 ton/yr
288,000 ton/yr
288,000 ton/yr
288,000 ton/yr
288,000 ton/yr
Total
TSP Emissions
(ton /year)
670
0.027
0.29
0.076
2.87
40
40
266
46
3
1068.3
Reapplication intensity = 1.0 gal of 12% solu-
tion/yd2
Application frequency = once every 44 days
Step 2 - Calculate the Required Number of Annual
Applications and Number of Treated Miles.
M , ., 365 days/yr
No. of annual applications = 44 days/application
= 8.3 applications/yr
No. of treated _ R « miles
miles per year ~ ' application
x 8.3
applications
yr
= 52 treated miles/yr
Step 3 - Select the Desired Program Implementation
Plan. The decision is made to purchase
rather than rent equipment. The implementa-
tion plan and associated costs are outlined In
Table 6-4. Scenario 2.
Step 4 - Calculate Total Annual Cost. To annualize
the capital investment, the capital cost shown
in Table 6-4, Scenario 2, is simply multiplied
by a capital recovery factor which is calcu-
lated as follows:
CRF =
(1 +i)n- 1
where:
i = annual interest rate fraction
n = number of payment years
30
-------
Assuming i = 0.15 and n = 10 years,
10
CRF = —
0.15 (1.15)
(1.15)'" -1
= 0.199252
The annual operation and maintenance costs (C0 )
are calculated as follows:
C0 = S4,785/treated mile x 52 treated miles/yr +
$630/actual mile x 6.3
= $253,000/yr
actual miles
yr
The total annualized cost (Ca ) Is:
Cn= CRF (Cp) + C0 +0.5(C0 )
= (0.199252) (105,000) +253,000
+ 0.5(253.000)
= $400.000
Because the costs in Table 6-4 are based on a road
width of 40 ft, it is necessary to scale total cost by
actual road width of 30 ft:
Actual total annualized cost = $400,000/yr x
= $300,000/yr
40 ft
Step 5 - Calculate Cost-Effectiveness (C*). Cost-ef-
fectiveness is defined as:
C* = ^i
AR
where
Ca = total cost from Step 4
AR = reduction in TSP emissions, i.e. the prod-
uct of the uncontrolled emission rate and
the fractional efficiency of control
C* -
$300,000/yr
770 ton/yr x 0.9
= $433/ton of TSP emissions reduced
The above calculations to determine the cost-effec-
tiveness ratio for control scenario 2 show that It will
cost approximately $433 to reduce emissions by
one ton from this unpaved road. To determine the
most cost-effective emissions reduction plan for the
Table 6-4. Cost Comparison for Two Selected
Implementation Scenarios"
Cost
Alternative Approach
Capital
investment
<$)
Unit O&M cost*
$/Treated
mile
$/Actual
mile
SCENARIO 1 —Rent Where Possible to Minimize Capital Expenditure
Purchase chemical and
ship in truck tanker 4,650
Store in contractor tank 140
Rent contractor grader to
prepare road 1,200
Take water from city line 20
Rent contractor truck" 500
5,310
1,200
SCENARIO 2-Buy Equipment Where Possible
Purchase chemical and
ship in truck tanker
Store in newly purchased
storage tank
Prepare road with plant
owned grader
Pump water from river or
lake
Apply chemical with plant
owned application
truckc
30,000
5,000
70,000
105,000
4,650
135
630
4,785
630
"1983 Dollars.
*Plant overhead costs are included.
'Includes labor to pump water and chemical and apply solution.
facility, a similar series of calculations must be per-
formed for each possible control scenario,
6.6 References
1. U.S. Environmental Protection Agency. Compi-
lation of Air Pollution Emission Factors, Volume
1: Stationary Point and Area Sources. Fourth
Edition, Supplement A, AP-42, Office of Air
Quality Planning and Standards, Research Tri-
angle Park, NC, October 1986.
2. Page, S.J. Evaluation of the Use of Foam for
Dust Control on Face Drills and Crushers. Rl
8595, U.S. Bureau of Mines, Washington,
D.C., 1982.
3. Elmutis, E.C., T.R. Blackwood, and R. Wach-
ter. Particulate Emissions from Stone Crushing
Operations. Monsanto Research Corporation,
Dayton, OH, November 1979.
4. Cusclno, T., Jr. Cost Estimates for Selected
Fugitive Dust Controls Applied to Unpaved and
Paved Roads In Iron and Steel Plants, Final Re-
port for Region V, U.S. Environmental Protec-
tion Agency, Chicago, IL, April 1984.
31
-------
-------
Appendix A
Glossary Of Terms
Air Quality Model - An equation or series of equa-
tions which predict a source Impact on air quality.
Annualized Cost - The control technique cost ($/yr)
calculated as annual cost over the useful life of the
equipment (or application). The annualized cost Is a
sum of the annualized purchase and installation cost
(I.e., capital costs) and the annual maintenance and
operating costs.
Application Frequency - Number of applications of a
control measure to a specific source per unit time;
equlvalently, the Inverse of time between two appli-
cations.
Application Intensity - Volume of water or chemical
solution applied per unit area of the treated surface.
Capital Recovery Factor - The factor which is used
to annualize capital investment to obtain the annual-
ized capital cost. The capital recovery factor is a
function of annual interest rate and the total number
of payment years.
Chemical Stabilization - The use of chemical dust
suppressants for the control of fugitive partlculate
emissions from open dust sources or material stor-
age piles.
Control Efficiency - Percent decrease in controlled
emissions from the uncontrolled state,
Cost-Effectiveness - The cost of control per unit
mass of reduced particulate emissions.
Dilution Ratio - Ratio of the number of parts of
chemical to the number of parts of solution, ex-
pressed in percent (e.g., one part chemical to four
parts water corresponds to a 20% solution).
Dry Sieving - The sieving of oven-dried aggregate
by passing it through a series of screens of de-
scending opening size.
Dust Suppressant - Water or chemical solution
which, when applied to an aggregate material, binds
suspendable particulate to larger particles.
Emission Factor - An estimate of the mass of un-
controlled emissions released to the atmosphere
per unit of source extent (e.g.. Ib/VMT).
Emission Rate - Mass of emissions generated per
unit time (e.g., Ib/hr).
Emissions Inventory - A listing and classification of
all sources of emissions, and the quantity of emis-
sions generated for a specific geographic area or
facility.
Erosion Potential - Total quantity of erodible parti-
cles, in any size range, present on the surface (per
unit area) prior to the onset of erosion.
Exposed Area - Outdoor ground area subject to the
action of wind and protected by little or no vegeta-
tion.
Exposure Profiling Method - A method for quantify-
ing fugitive emissions which involves the isokinetic
measurement of airborne pollutant immediately
downwind of the source by means of simultaneous
multipoint sampling over the effective plume cross
section.
Fine Particulate (FP) - Particulate matter less than
or equal to 2.5 (im in aerodynamic diameter.
Fugitive Dust - Solid particles generated by the ac-
tion of wind or machinery which are not emitted from
a stack, duct, or flue.
Fugitive Emissions - Emissions not originating from
a stack, duct, or flue.
Inhalable Particulate (IP) - Particulate matter less
than or equal to 15 p,m aerodynamic diameter.
Materials Handling - The receiving and transport of
raw, intermediate and waste materials, including
barge/railcar unloading, conveyor transport and as-
sociated conveyor transfer and screening stations.
Moisture Content - The mass portion of an aggre-
gate sample consisting of unbound moisture as de-
termined from weight loss In oven drying.
Open Dust Sources - Sources of fugitive emissions
that entail generation of particulate matter by the
forces of wind or machinery acting on exposed
(i.e., open) materials where no physical or chemi-
cal change occurs to the particle-generating mate-
rial.
Particle Diameter, Aerodynamic - The diameter of a
hypothetical sphere of unit density (1 p,g/cm3) hav-
33
-------
ing the same terminal settling velocity as the particle
in question, regardless of its geometric size, shape
and true density.
Physical Stabilization - Any measure which physi-
cally reduces the emissions potential of a source re-
sulting from either mechanical disturbance or wind
erosion.
PMio - Particulate matter less than or equal to 10
urn in aerodynamic diameter.
Preventive Measures - Techniques for controlling
fugitive particulate emissions which prevent the
creation and/or release of particulate matter(e.g.,
wet suppression, stabilization of unpaved surfaces,
cleaning of paved surfaces).
Process Sources - Sources of fugitive emissions
associated with industrial operations that alter the
chemical or physical characteristics of a feed-
material.
Receptor-Oriented Air Quality Model (Receptor
Model) - An air quality model which uses chemical
analysis at receptors (i.e., ambient monitors) to
statistically infer the separate contribution from each
of the sources of the emissions.
Respirable Particulate (RP) - Particulate matter less
than or equal to about 3.5 prn aerodynamic diame-
ter, as measured with a 10-mm Door-Oliver cy-
clone precollector.
Road, Paved - A roadway constructed of rigid sur-
face materials such as asphalt, cement, concrete,
and brick.
Road, Unpaved - A roadway constructed of nonrigid
surface materials such as dirt, gravel (crushed
stone or slag), and oil and chip surfaces.
Road Surface Dust Loading - The mass of loose sur-
face dust on a paved roadway, per length of road-
way, as determined by dry vacuuming.
Road Surface Material - Loose material present on
the surface of an unpaved road.
Silt Content - The mass portion of an aggregate
sample smaller than 75 p.m in diameter as deter-
mined by dry sieving,
Source Extent - The measure of the level of source
activity. For roads, the source extent is in vehicle
miles traveled (VMT) per year.
Source-Oriented Air Quality Models (Dispersion
Models) - An air quality model which predicts a
source's impact on air quality by using a series of
predictive equations to model the dispersion of the
plume from the source.
Total Particulate (TP) - Particulate matter of all
sizes as collected by isokinetic sampling.
Total Suspended Particulate (TSP) - Particulate
matter measured by a high volume sampler with an
inlet 50% cutoff 30-50 jam in aerodynamic diameter.
Upwind/Downwind Method - A method of quantify-
ing fugitive emissions which involves the measure-
ment of air quality upwind and downwind of the
source under known meteorological conditions, fol-
lowed by "back-calculation" of source emission
rates using atmospheric dispersion equations.
Vehicle, Heavy-Duty - A motor vehicle with a gross
vehicle traveling weight exceeding 30 tons.
Vehicle, Light-Duty - A motor vehicle with a gross
vehicle traveling weight of less than or equal to 3
tons.
Vehicles, Medium-Duty - A motor vehicle with a
gross vehicle traveling weight of greater than 3
tons, but less than 30 tons.
VKT - Vehicle kilometers traveled.
VMT - Vehicle miles traveled.
Wet Suppression - The application of water to the
surface of the material producing emissions to inhibit
the generation of particulate matter emissions.
34
-------
Appendix B
Abbreviations
a - Constant in equation 3-2
ADT - Average daily traffic
AMS - American Meteorological Society
APCA - Air Pollution Control Association
AP-42 - The following publication: U.S, Environ-
mental Protection Agency. Compilation of Air
Pollution Emission Factors, Volume 1: Stationary
Point and Area Sources. Fourth Edition, Supple-
ment A, AP-42, Office of Air Quality Planning and
Standards. Research Triangle Park, NC, October
1986.
Avg - Average
b - Constant In equation 3-2
c - Fractional control efficiency
C* - Cost-effectiveness
Ca - Annuallzed costs of control equipment
CaCI2 - 'Calcium chloride
CBR - California bearing ratio
CCSEM - Computer controlled scanning electron
microscopy
cfm - Cubic feet per minute
Chem - Chemical
C0 - Direct operating costs
CO - Colorado
Cp - Installed capital costs
CRF - Capital recovery factor
c(t) - Instantaneous control efficiency at t days af-
ter application of dust suppressant
C(T) - Average control efficiency during period of T
days between application of dust suppressant
d - Average hourly daytime traffic rate
days/yr - Days per year
dt - Change in time
E - Emission factor
EPA - U.S, Environmental Protection Agency
f - Percent time unobstructed wind speed greater
than 12 miles per hour
FP - Fine paniculate matter
ft - Feet
gal - Gallons
gal sol/yd2 - Gallons of solution per square yard
gal/yd2 - Gallons per square yard
g/m2 - Grams per square meter
g/m3 - Grams per cubic meter
g/s - Grams per second
H - Drop height except as used in Appendix C
H - Final plume rise in Gaussian equation in
Appendix C
H2O - Water
hc - Height of convectively mixed layer
hi-vol - High volume
hm - Height of mechanically mixed layer
hr- Hour
hr 1 - Per hour
nr/yr - Hours per year
I - Interest rate except as used in equation 5-2
i - Application intensity In equation 5-2
I - Road augmentation factor
IL - Illinois
in - Inches
IP - Inhalable partlculate
k - Particle size multiplier
kg - Kilogram
kg/VKT - Kilogram per vehicle kilometer traveled
km - Kilometer
km/hr - Kilometers per hour
I - Liters
L - Surface dust loading
l/m2 - Liters per square meter
Ib - Pound
Ib/mlle - Pounds per mile
-------
Ib/VMT - Pounds per vehicle mile traveled
m - Meters except as used In equation 3-2
m - Road surface moisture content only as used In
equation 3-2
M - Material moisture content as used on Tables 6-1
and 6-2
M - Annual source extent except as used In Tables
6-1 and 6-2
m2 - Square meters
m3 - Cubic meter
m3/hr - Cubic meters per hour
mg - Megagram
MgC!2 - Magnesium chloride
mm - Millimeter
mm/hr - Millimeters per hour
MN - Minnesota
mph - Miles per hour
MRI - Midwest Research Institute
m/s - Meters per second
n - Number of travel lanes as used in Tables 6-1 and
6-2
n - Economic life of control system except as used
in Tables 6-1 and 6-2
NC - North Carolina
NCDC - National Climatic Data Center, Ashevllle, NC
No. - Number
OH - Ohio
O&M - Operation and maintenance
oz/yd2 - Ounces per square yard
p - Number of days with at least 0.01 inches of pre-
cipitation per year except as used In equation
5-2
p - Potential average hourly daytime evaporation
rate in equation 5-2
P - Profiling, used in Table 5-4
PA - Pennsylvania
PMio - Particulate matter consisting of particles less
than or equal to 10 prn
Q - Source strength
RP - Respirable particulate matter
s - Silt content of road surface material
S - Mean vehicle speed
SEM - Scanning electron microscope
sL - Silt loading
Sol - Solution
SORI - Southern Research Institute
ST - Short tons
t - Time between applications In equation 5-2
TEM - Transmission electron microscope
ton/yr - Tons per year
TP - Total airborne particulate matter
TSP - Total suspended particulate matter
u - Wind speed as used In Appendix C
U - Mean wind speed except as used In Appendix C
U/D - Upwind/downwind, used In Table 5-4
UMAMAP - User's Network for Applied Modeling of
Air Pollution
Veh - Vehicle
VKT - Vehicle kilometer traveled
VMT - Vehicle mile traveled
VMT/yr - Vehicle miles traveled per year
w - Mean number of wheels
W - Mean vehicle weight
X - Pollutant concentrations
y - Lateral distance from plume centerllne In Gauss-
Ian equation in Appendix C
Y - Average capacity (of dumper or front end
loader) except as used In Appendix C
yd2 - Square yard
yd3 - Cubic yards
yr - Year
AR - Annual reduction in particulate emissions
AT - Temperature difference
© - Wind direction
g/m3 - Mlcrogram per cubic meter
urn - Micrometer
umA - Micrometer (aerodynamic basis)
ay - Lateral dispersion parameter
az - Vertical dispersion parameter
36
-------
Appendix C
Modeling Of Fugitive Emissions
The Identification and estimation of air quality Im-
pacts from fugitive dust sources typically require the
use of air quality models. Traditional regulatory ap-
proaches have dictated that source Impacts be
Identified by dispersion (source) modeling. The fol-
lowing discussion is intended to provide a general
overview of source-oriented models; for more de-
tailed discussions, the user should consult recent
reviews readily available In the scientific litera-
ture. [1,2] Source-oriented models assume that
mass transported from a source to a receptor is
transported with conservation of mass by atmos-
pheric dispersion of the source material. [3] It
should also be recognized that the selection of an
appropriate model (s) will depend upon the particular
program/study objectives and resource constraints
(i.e., data, manpower, computing facilities, etc.),
as well as the user's knowledge of the model tech-
nology.
The Gaussian plume model Is more widely used than
any other source-oriented model. Stripped to its es-
sentials, the Gaussian model may be represented as
follows:
x =
(C-1)
where the parameters are:
X (g/m3) = concentration of pollutant in air
Q (g/s) = continuous point source strength
u (m/s) = wind speed at height H
ay (m) = lateral dispersion parameter
crz (m) = vertical dispersion parameter
y (m) = lateral distance from plume center
line
z (m) = height above ground
H (m) = final plume rise of plume above
ground
As the name implies, the model predicts concentra-
tions under the assumption that the plume disperses
in the horizontal and vertical according to a Gaussian
distribution. Other major assumptions include: a)
constant and continuous emission rates, b) no vari-
ations in meteorology (wind speed, wind direction,
and atmospheric stability) between source and re-
ceptor, and c) complete reflection of the plume
from the ground surface.
The Gaussian plume concept is the basis for nearly
all models in the U.S. EPA system of UNAMAP (Us-
er's Network for Applied Modeling of Air Pollution)
models. The differences between models of the UN-
AMAP family are mostly due to variations in the treat-
ment of plume rise, pollutant half-life, diffusion limi-
tations due to mixing heights, source configurations,
and dispersion coefficients to characterize plume
growth. Abstracts which summarize model capabili-
ties of most of the current generation of UNAMAP
models may be found elsewhere. [4] Reasonably
complete technical descriptions for each model are
available In the various users' manuals.
For all but the crudest screening applications, the
use of a dispersion model requires appropriate infor-
mation on source emission rates and study area me-
teorology. In the case of stationary sources, it is
usually a fairly straightforward procedure to develop
an adequate emissions inventory. For fugitive (par-
ticularly open source) emissions, the measures of
source extent (e.g.. unvegetated surface area ex-
posed to the wind) are often more difficult to define.
The reliability of open source emissions estimates
are greatly increased if site-specific information is
collected.
In similar fashion, to make the best use of Gaussian
modeling, site-specific meteorological measure-
ments need to be made that relate closely to pollut-
ant dispersion. [5] These include, for example, a)
continuous measurements of wind speed (u) and di-
rection (@) at two heights; b) ambient temperature
difference (AT) between 2 and 10 m: and c)
heights of the convectively mixed layer (hc) and the
mechanically mixed layer (hm). Very few programs
are designed to acquire such detailed information.
37
-------
Many routine modeling applications rely on data from
nearby locations such as airports, National Weather
Service stations, and military Installations to repre-
sent the atmospheric conditions for the area of In-
terest. These observations are intended primarily for
aviation needs, and are not particularly well suited to
dispersion problems. The primary source for sur-
face and upper air meteorological data is the Na-
tional Climatic Data Center (NCDC, Asheville, NC).
For many long-term or climatological applications,
the meteorological conditions of a site are repre-
sented by a stability array or "STAR" tabulation. The
STAR tabulation summarizes meteorological condi-
tions in terms of joint frequency distributions of wind
speed, atmospheric stability class, and wind direc-
tion. This information has been developed for many
locations in the United States and is also available
from NCDC.
The principal advantage of source-oriented (disper-
sion) models lies in the fact that they can be used to
directly predict the impact of either existing or pro-
posed sources. [3] Another advantage of this class
of models is that they do not require ambient air
quality data, though, if available, air quality data may
be used to assign "background" pollutant levels.
Additional advantages are that the models are widely
available and have been evaluated using many dif-
ferent data sets. [2]
The primary limitations of dispersion models relate
not only to deficiencies in the quality of the Input
data for a particular application, but also to the abil-
ity of the Gaussian model to reproduce the impor-
tant physical/chemical processes affecting trans-
port of pollutants in the atmosphere. The Gaussian
model will perform best under the conditions used to
form the basis for the current models. These condi-
tions include:
Source: Low-level, continuous, nonbuoyant emis-
sions, in simple terrain.
Meteorology: Near neutral stability, steady, and
relatively homogeneous wind field.
Estimate: Local, short-term, concentrations of In-
ert pollutants.
Under those relatively simple conditions, "factor of
two" agreement between predicted and observed
concentrations is probably realistic. [6]
Addition of complicating features to the simple dis-
persion case will substantially increase the uncer-
tainties associated with model estimates. Compli-
cating features include:
• Aerodynamic wake flows of all kinds
• Buoyant fluid flows and accidental releases of
heavy toxic gases
• Flows over surfaces markedly different from
those represented In the basic experiments,
e.g., forests, cities, water, complex terrain
• Dispersion in extremely stable and unstable
conditions
• Dispersion at great downwind distances (>10
to 20 km)
It is widely recognized that significant improvements
In dispersion modeling will require more direct ob-
servational knowledge under these conditions.
Model users should be aware that the capabilities of
the current UNAMAP series to represent these fea-
tures are based on a few special case studies. (7]
References
1. Turner, D.B. Atmospheric Dispersion Modeling:
A Critical Review. Journal of the Air Pollution
Control Association. 29 (5):502-519, 1979.
2. Hanna, S.R. Handbook on Atmospheric Diffu-
sion Models. ATDL-81/5, National Oceanic and
Atmospheric Administration. Oak Ridge, TN,
1981.
3. Cooper, J.A. Chemical Mass Balance Source
Apportionment Methods. Paper presented at
the 74th Annual Meeting of the Air Pollution
Control Association, Philadelphia, PA, 1981.
4. Environmental Protection Agency. Environ-
mental Modeling Catalogue: Abstracts of Envi-
ronmental Models. U.S. EPA Information Clear-
inghouse. U.S. Environmental Protection
Agency, Washington, DC, August 1982.
5. American Meteorological Society. On-Site Me-
teorological Requirements to Characterize Dif-
fusion from Point Sources. Proceedings from a
Workshop Held in Raleigh, NC, January 15-17,
1980. American Meteorological Society, Bos-
ton, MA. 1980.
6. American Meteorological Society. Accuracy of
Dispersion Models: A Position Paper of the AMS
Committee on Atmospheric Turbulence and
Diffusion. Bulletin of the American Meteorologi-
cal Society, 59(8): 1025-26, 1978.
7. American Meteorological Society. Air Quality
Modeling and the Clean Air Act: Recommenda-
tions to EPA on Dispersion Modeling for Regula-
tory Applications. American Meteorological
Society, Boston, MA, (NTIS PB 83-106237),
1980.
-------
Appendix D
Control Efficiency Decay Curves
Figure D-1. Control efficiency decay for an initial application of Petro Tac [1-3].
Petro-Tatr
o
g>
"o
it
in
o
O
100
80
60
40
20
Rating A
Application Intensity 3.2 |im
Dilution Ratio 20%
Avg. Veh. Weight 27 Mg
Avg, No, of Wheels 9.2
Avg. ADTa 414
100
10
Vehicle Passes after Application
(1000's)
Vehicle Passes after Application
(1000's)
ADT = average daily traffic (vehicles/day)
-------
Figure D-2. Control efficiency decay for an initial application of Coherex [1-3]
Rating B
c:
o
o
100
80 —
60 —
20
Coherex®
Application Intensity
Dilution Ratio
Avg. Veh. Weight
Avg. No. of Wheels
Avg, ADT
3 . 8 |1 m2
20%
34 Mg
6,2
95
TP
IP
o
O
100
80 —
60 —
o 40
20
PM
10
FP
J_
1234
Vehicle Passes after Application
(1000's)
1234
Vehicle Passes after Application
(1000's)
40
-------
Control Efficiency (%)
Control Efficiency (%)
S"
o a>
o ;?
ct>
>
•a
•
o>
o
(O
o
> >
td z
0 0
H -
°
^
3-
id
<
3~
§
(B
03"
^
CO
•g.
o"
o"
5-
CD"
^
2.
<
-O.
in
3
O
o
(D
X
O
s
IB
3!
o
(0
o
0
0
5'
o
3
O
0
a
I
u
-------
Figure D-4. TSP control efficiency decay for light-duty traffic on unpaved roads [4],
120
100 —
80 —
9
o
UJ
o
o
o
Q.
w
t-
60 —
40 —
20
Reapplicatlon of OWB (0.6 gal sol/yd )
Application
Intensity (gal sol/yd 2 )
Dilution Ratio (gal chem:gal H2O
Avg Veh. Weight (tons)
Avg. No of Wheels
Rating
B C
Flambinder® Oil
& Arcote Well
220® Brine
• *
1.9 3.8
1:4 1:0
3 3
4 4
C
Coherex®
•
1.5
1:4
3
4
12
18
24
30
36
Time after Application (days)
42
-------
FP Control Efficiency (%)
TSP Control Efficiency (%)
TI
IQ
o>
O
oo
O
ro A
o - o
O3
o
o
o
£<"
I *
*?°
(A
^»
*
>
-a _j
1=0
oU
01 O
5:01
I 1
o
o U
9" O
< en
CD
3;
O.
ID
SS
SSD > g >
••*• (Q (Q C -^j
(O -7- ^ S" o"
Z. -C 3 ja
0 ' 2. 3
1 1- s 1
in
(D ^* ^:
Q.
0 J»
0
O
O
0
O
en
03
i
CO
O)
-^
1
oo
to
o
1
N>
o>
?
O5
— k
on
— *
to
o
fo
1
o
CD
o
KJ
1
o
en
0
i
i
o
b>
T
4)
_
IV)
3*-
OJ
J3
O
I
O
I
"3
-------
Figure D-6. Control efficiency decay for Soil Sement and Biocat-Enzyme applied to haul roads [5].
Soil Sement ®
Mine
Application Intensity (gal sol/yd2)
Dilution Ratio (gal chem:gal H2O
Avg. Veh. Weight (tons)
Avg, No. of Wheels
Rating
1
1.9-3.0
1:8,3
22-89
10
B
2
1.0
1:6.4
38-82
10
B
Biocat-Enz
3
2.0
1:20.000
70-276
6
D
o
c
0)
o
o
o
CL
V)
100
80
60
40
20
Topical
Application
Mixed
Application
100
o
c
9!
o
UJ
o
c
o
o
40 ._
20 -
5 10 15 20 25
Vehicle Passes after Application
(1000's)
30
Mixed
Application
5 10 15 20 25
Vehicle Passes after Application
(1000's)
30
44
-------
Figure D-7. Control efficiency decay for Flamblnder applied to haul roads [5].
Flambinder
s
o
§
o
a.
v>
100
80
60
40
20
Mine
Application Intensity (gal sol/yd2)
Dilution Ration (gal chenv. gal H2O
Avg. Veh. Weight (tons)
Avg. No. of Wheels
Rating
1
0.5-2.1
1:4.6
16-65
10
E
2
0.5-2.0
1:4.6
51-69
10
C
3
1.8
1:4.6
70-276
6
C
Topical
Application
Mine 1
. Mine 1
Mixed
Application
>,
o
E
ill
§
O
fc
100
80
60
40
20
Topical
Application
I
I
I
5 10 15 20 25
Vehicle Passes after Application
(1000's)
30
Mixed
Application
5 10 15 20 25
Vehicle Passes after Application
(1000's)
30
-------
Figure D-8. Control efficiency decay for Arco 2200® applied to haul roads [5].
Arco 2200 <
Mine
Application Intensity (gal sol/yd 2 )
Dilution Ratio (gal chem:gal H2O
Avg. Veh. Weight (tons)
Avg. No. of Wheels
Rating
2
0,9-2.8
1:7
18-80
10
C
3
1,1-2.3
1:6.1
70-276
6
E
100
8
$
g
8
o
a
w
H
Topical
Application
Mine 3
Mixed
Application
-Mine 3
100
o
i
o
LU
O
Q.
U.
Mine 3
Topical
Application
5 10 15 20 25
Vehicle Passes after Application
(1000's)
30
5 10 15 20 25
Vehicle Passes after Application
(1000's)
46
-------
Figure D-9. Control efficiency decay for reapplication of various chemical suppressants [6].
Code Letter
Initial Application
Intensity (gal /yd2)
Second Application
Intensity (gal /yd2)
Dilution Ration (chem:
water)
Avg. Vehicle Weight (tons)
Avg. No. of Wheels
Petro Tac®
P
0.21
0,35
1:5
9.7 - 24
6
Soil Sement®
S
0.16
0.44
1:5
9.6 - 24
6
Cohered
C
0.21
0.36
1:5
9.6 - 24
6
Generic
G
0.14
0.46
1:5
9.3 - 24
6
100
o
0)
o
LU
o
5?
100
50
PM
I I I I I
IP
10 20 30 0 10 20
Days after Second Application
47
-------
References
1. Cuscino, T., Jr., G.E. Muleski, and C. Cow-
herd, Jr. Iron and Steel Plant Open Source Fu-
gitive Emission Control Evaluation.
EPA-600/2-83- 110, U.S. Environmental Pro-
tection Agency, Research Triangle Park, NC,
October 1983.
2. Muleski. G.E., T. Cuscino, Jr., and C. Cow-
herd, Jr. Extended Evaluation of Unpaved Road
Dust Suppressants in the Iron and Steel Indus-
try. EPA-600/2-84-027, U.S. Environmental
Protection Agency, Research Triangle Park,
NC. February 1984,
3. Cowherd, C., Jr.. R. Bohn, and T. Cuscino, Jr.
Iron and Steel Plant Open Source Fugitive
Emission Evaluation, EPA-600/2-79-103,
(NTIS PB--299 385), U.S. Environmental Pro-
tection Agency. Research Triangle Park. NC,
May 1979.
4. Russell, D. and S. C. Caruso. A Study of
Cost-Effective Chemical Dust Suppressants
for Use on Unpaved Roads in the Iron and Steel
Industry. American Iron and Steel Institute, De-
cember 1982.
5. Rosbury, K. D., and R. A. Zimmer. Cost-Effec-
tiveness of Dust Controls Used on Unpaved
Haul Roads - Volume 1 of 2. Draft Final Report,
U.S. Bureau of Mines, Minneapolis, MN, De-
cember 1983.
6. Muleski, G.E. and C. Cowherd, Jr. Evaluation
of the Effectiveness of Chemical Dust Sup-
pressants on Unpaved Roads. U.S. Environ-
mental Protection Agency. Research Triangle
Park, NC, Draft Final Report, May 1986.
U. b. GOVEBNMENT PRIMTISG OFFICE 1987/748-121/67021
48
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