United States
Environmental Protection
Agency
Industrial Environmental Research
Laboratory
Research Triangle Park NC 27711 ''
Research and Development
EPA-600/S2-83-110 Jan. 1984
&ER& Project Summary
Iron and Steel Plant Open
Source Fugitive Emission
Control Evaluation
Thomas Cuscino, Jr., Gregory E. Muleski, and Chatten Cowherd, Jr.
Open dust sources in the iron and
steel industry were estimated to emit
88,800 tons/year suspended particulate
in 1978 based on a 10 plant survey. Of
this, 70, 13, and 12% were emitted by
vehicular traffic on unpaved roads, vehi-
cular traffic on paved roads, and sto-
rage pile wind erosion, respectively.
Emission measurements, utilizing the
exposure profile technique, indicate a
17% solution of a petroleum resin (Co-
herex") in water on an unpaved road re-
duced heavy-duty vehicle emissions by
95.7% for total particulate, 94.5% for
particulate <15 /urn, and 94.1 % for par-
ticulate <2.5 /urn (averaged over the
first 48 hours after application). Plain
water reduced emissions 95% for all
particle sizes half an hour after applica-
tion. Four hours later, efficiency of wa-
tering had dropped to 55% (total),
49.6%(<15/um),and61.1%«2.5Aim).
CoherexR on an unpaved road travelled
by light-duty vehicles reduced emis-
sions by 99.5% (total), 98.6% «15
Aim), and 97.4% (<2.5 /mi), 25 hours
after application. Control efficiency de-
cayed to 93.7% (total), 91.4% «15
fjm), and 93.7% (<2.5 yum), 51 hours
after application.
On paved roads, vacuum sweeping
reduced emissions 69.8% (total), 50.9%
«15 yum), and 49.2% «2.5 yum), 2.8
hours after vacuuming. Forty minutes
after water flushing, emissions were
reduced by 54.1% (total), 48.8% (<15
yum), and 68.1% (<2.5yum). Combined
flushing and broom sweeping reduced
emissions by 69.3% (total), 78.0% «15
fjm), and 71.8% (<2.5/urn), 40 minutes
after application.
Control of emissions from coal
storage piles varied from 90% to almost
zero depending on the type of treatment,
length of time since treatment was
applied, and windspeed. Tests were
performed using a portable wind tunnel.
Relationships were developed to
determine relative cost effectiveness of
open source emission controls.
This Project Summary was developed
by EPA's Industrial Environmental
Research Laboratory, Research Triangle
Park, NC, to announce key findings of
the research project that is fully docu-
mented in a separate report of the same
title (see Project Report ordering
information at back).
Introduction
Previous studies of open dust particulate
emissions from integrated iron and steel
plants provided strong evidence that open
dust sources (e.g., vehicular traffic on
unpaved and paved roads, aggregate
material handling, and wind erosion)
should occupy a prime position in control
strategy development. These conclusions
were based on comparability between
industry-wide uncontrolled emissions
from open dust sources and typically
controlled fugitive emissions from major
process sources such as steelmaking
furnaces, blast furnaces, coke ovens, and
sinter machines. Moreover, preliminary
cost-effectiveness analysis of promising
control options for open dust sources
indicated that control of open dust
sources might result in significantly
improved air quality at a lower cost in
relation to control of process sources.
(Cost-effectiveness is defined as dollars
expended per unit mass of particulate
emissions prevented by control.) These
preliminary conclusions warranted gather-
-------
ing more definitive data on control
performance and costs for open dust
sources in the steel industry.
The cost reduction potential of open
dust sources has not been missed by the
iron and steel industry. With the advent of
the Bubble Policy (Alternative Emissions
Reduction Options) on December 11,
1979 (revised April 7,1982), the industry
recognized the economics of controlling
open dust sources as compared to
implementing more costly controls on
stack and process fugitive sources of
particulate emissions. However, the
Bubble Policy requires the demonstration
of no net gain in emissions from an
imaginary bubble surrounding the plant.
To demonstrate no net gain in emissions
as a result of a proposed controlled
trading scenario, the controlled emission
rate for an open dust source must be
estimated using the equation:
R = Me( 1-C)/2,000
where: R = mass emission rate, tons*/
year
M = annual source extent
•(Although EPA policy requires using metric units,
certain nonmetric units are used m this summary
for clarity. Readers more familiar with metric units
may use the conversion factors at the end of this
summary
e = uncontrolled emission factor
(i.e., pounds of uncontrolled
emissions per unit of source
extent)
C = overall control efficiency,
expressed as a fraction.
Values for uncontrolled emission factor,
e, can be calculated using the predictive
emission factor equations shown in Table
1. These predictive equations are the
outcomes of numerous prior MRI field
tests. Parameters (e.g., moisture and silt
contents of the emitting material, or
equipment characteristics) which may
affect particulate emission levels from
open sources were identified and measured
Table 1. Open Dust Emission Factors Experimentally Determined by MRI
Source Category Measure of Extent
Emission Factor*
Ib/unit of source extent
Correction Parameters
Unpaved roads
Paved roads
Batch load-in (e.g.,
front-end loader,
railcar dump)
Continuous load-in
(e.g., stacker,
transfer station)
Vehicle-miles traveled 5.9
Vehicle-miles traveled 0.09 I
Tons of material loaded in 0.0018
/wdo/d) u) (3^5
10 / \ 7,0007 \ 3
Tons of material loaded in 0.0018
Active storage pile Tons of material put
maintenance and traffic through storage
Active storage pile wind Tons of material put
erosion through storage
1.5J235
0.05 /_£_\ /_2_1 fJL-\fJ±.
Batch load-out (e.g.,
front-end loader,
railcar dump)
Wind erosion of
exposed areas
Tons of material loaded out 0.0018
Acre-years of exposed land 3.400
(7X7X7)
(f)W
so
s = Silt content of aggregate or road
surface material, %
S = Average vehicle speed, mph
W = Average vehicle weight, tons
L = Surface dust loading on traveled
portion of road, Ib/mile
U = Mean wind speed at 4 m above
ground, mph
M - Unbound moisture content of
aggregate or road surface
material, %
Y = Dumping device capacity, yd3
K = Activity factor*
d = Number of dry days per year
f = Percentage of time wind speed
exceeds 12 mph at 1 ft above
the ground
D = Duration of material storage, days
e = Surface erodibility, tons/acre/year
P-E = Thornthwaite's Precipitation-
Evaporation Index
N = Number of active travel lanes
I - Industrial road augmentation
fatorc
w = Average number of vehicle wheels
h = Drop height, ft
F = Percentage of time unobstructed
wind speed exceeds 12 mph at mean
pile height
"Particulate smaller than 30 um in diameter based on particle density of 2.5 g/cm3.
^Equals 1.0 for front-end loader maintaining pile tidiness and 50 round trips of customer trucks per day in the storage area.
"Equals 7.0 for trucks coming from unpaved to paved roads and releasing dust from vehicle underbodies;
Equals 3.5 when 20% of the vehicles are forced to travel temporarily with one set of wheels on an unpaved road berm while passing on narrow roads;
Equals 1.0 for traffic entirely on paved surface.
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during the testing process. For sources
with a sufficient number of tests, multiple
linear regression formed the basis upon
which significant variables were identified
and then used in developing the predictive
equation.
The annual source extent can be
estimated by plant management from
plant records and discussions with
operating personnel. The variable with
the least accurate data to support an
estimate of controlled emissions is the
control efficiency. Table 2 summarizes
open dust source controls that are or have
been used in the iron and steel industry.
Control efficiency values are needed for
all the techniques shown in Table 2.
Variables Affecting
Control Efficiency
Open dust source control efficiency
values can be affected by four broad
categories of variables: time-related
variables, control application variables,
equipment characteristics, and charac-
teristics of surface to be treated.
Time-Related Variables
Because of the finite durability of all
surface-treatment control techniques,
ranging from hours (watering) to years
(paving), it is essential to tie an efficiency
value to a frequency of application (or
maintenance). For measures of lengthy
durability, the maintenance program
required to sustain control effectiveness
should be indicated. One likely pitfall to
be avoided is the use of field data on a
freshly applied control measure to
represent the lifetime of the measure.
The climate, for the most part, accele-
rates the decay of control performance
adversely through weathering. For exam-
ple, freeze/thaw cycles break up the
crust formed by binding agents; precipita-
tion washes away water-soluble chemical
treatments like lignin sulfonates, and
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, and will definitely
improve efficiency of watering.
Control Application Variables
The control application variables affect-
ing control performance are: application
intensity, application frequency, dilution
ratio, and application procedure. Applica-
tion intensity is the volume of solution
placed on the surface per unit area of sur-
face: the higher the intensity, the better
the expected control efficiency. However,
Table 2. Summary of Potential Open Dust Source Control Techniques
Source
Control technique
Unpaved roads and parking lots.
Paved roads and parking lots.
Material handling and storage
pile wind erosion
Conveyor transfer stations.
Exposed area wind erosion.
A. Watering
B. Chemical treatment
C. Paving
D. Oiling
A. Sweeping
1. Broom
a. Wet
6. Dry
2. Vacuum
B. Flushing
A. Watering
B. Chemical treatment"
A. Enclosures
B. Water sprays
C. Chemical sprays"
A. Watering
B. Chemical treatment"
C. Vegetation
D. Oiling
"For example: salts, lignin sulfonates. petroleum resins, wetting agents, and latex binders.
this relationship applies only to a point,
because too intense an application will
begin to run off the surface. The point
where runoff occurs depends on the
slope and porosity of the surface.
Equipment Characteristics
Equipment characteristics that affect
control efficiency values are those
involved in imparting energy to the
treated surface which might break the
adhesive bonds keeping fine particulate
composing the surface from becoming
airborne. For example, vehicle weight
and speed can affect the control efficiency
for chemical treatment of unpaved roads.
An increase in either variable accelerates
the decay in efficiency. Figure 1 is a
general plot portraying the change in rate
of decay of the control efficiency for a
chemical suppressant applied to an
unpaved road as a function of vehicle
speed, weight, and traffic volume.
Characteristics of Surface to be
Treated
Surface characteristics that contribute
to the breaking of a surface crust will
affect control efficiency. For example, for
unpaved road controls, road structure
characteristics affect control efficiency.
These characteristics are: combined
subgrade and base bearing strength,
amount of fine material (silt and clay) on
the surface of the road, and the friability
of the road surface material. Unaccept-
able values for these variables mainly af-
fect the performance of chemical con-
trols. Low bearing strength causes the
road to flex and rut in spots with the pass-
age of heavy trucks; this destroys the
compacted surface enhanced by the
chemical treatment. A lack of fine materi-
al in the wearing surface deprives the
chemical treatment of the increased
particle surface area necessary for in-
terparticle bonding. Finally, the larger
particles of a friable wearing surface ma-
terial simply break up under the weight of
the vehicles and cover the treated road
with a layer of untreated dust.
Project Objectives
The overall objective of this project was
to provide data that will document quan-
tities of particulates generated from con-
trolled open dust sources at steel plants
and the cost-effectiveness of control pro-
cedures for eliminating or reducing emis-
sions. Required to achieve the above
objective were:
• Field tests to measure emissions
from open dust sources to deter mi ne
the efficiency of selected control
procedures.
• Evaluation of data obtained in the
test program to determine the
change in efficiency over time.
-------
700
Increasing Vehicle
Speed, Weight, and
Traffic Density
Time After Application
Figure 1. Effect of vehicle speed, weight, and traffic density on control performance.
• Development of design and operat-
ing information on all control
procedures evaluated, including
optimum operating procedures;
operator and material requirements;
design parameters; capital, operat-
ing, and maintenance costs; and
energy requirements.
Report Structure
The report is structured as follows:
Section 2.0 gives results of a 10-plant
survey to determine the extent of open
dust sources and controls in the iron and
steel industry; Section 3.0 contains the
methodology and results of source
testing via exposure profiling; Section 4.Q
contains the methodology and result of
wind erosion testing via a portable wind
tunnel; Section 5.0 presents cost, design,
and operating information related to
control techniques; and Sections 6.0
through 8.0 present references, a glossary,
and English-to-metric conversion units,
respectively.
Numbers in the report are generally
rounded to three significant figures;
therefore, columns of numbers may not
add to the exact totals listed. Rounding to
three significant figures produces a
rounding error of less than 0.5%.
Summary and Conclusions
The purpose of this study was to
measure the control efficiency of various
techniques used to mitigate emissions
from open dust sources in the iron and
steel industry, such as vehicular traffic on
unpaved and paved roads and wind
erosion of storage piles and exposed
areas. The control efficiency was deter-
mined not only for total paniculate (TP),
but also for inhalable particulate (IP)—
particles less than 15//m in aerodynamic
diameter, and for fine particulate (FP)—
particles less than 2.5/um in aerodynamic
diameter. In addition to control efficiency
measurement, parameters defining control
design, operation, and cost were quantified.
The methodology for achieving the
above goals involved the measurement of
uncontrolled and controlled emission
factors for emissions from vehicular
traffic on unpaved roads, vehicular traffic
on paved roads, and storage pile wind
erosion. These sources were selected
based on an open dust source emission
inventory for the iron and steel industry
which showed the above three sources to
contribute 70.4%, 12.7%, and 11.5%,
respectively, of the 88,800 T/yr of
suspended particulate emitted by the
industry.
The exposure profiling method developed
by MRI was the technique utilized to
measure uncontrolled and controlled
emission factors from vehicular traffic on
paved and unpaved roads. Exposure
profiling of roadway emissions involves
direct isokinetic measurement of the total
passage of open dust emissions approxi-
mately 5 m downwind of the edge of the
road by simultaneous sampling at four or
five points distributed vertically over the
effective height of the dust plume. Size
distributions were measured at 1 and 3 m
heights downwind utilizing cyclone
precollectors followed by parallel slot
cascade impactors. During selected tests,
size selective inlets mounted on high
volume samplers were also deployed
downwind.
Nineteen tests of controlled and
uncontrolled emissions from vehicular
traffic on unpaved roads were performed.
Ten tests were of heavy-duty traffic
(greater than 30 tons) and nine were of
light-duty traffic (less than 3 tons).
In calculating the efficiency of a control
technique from emission factor measure-
ments collected during controlled and
uncontrolled tests, the effect of testing
during different periods in the lifetime of
the control was taken into account. The
decay of control efficiency with time after
application has a number of causes, such
as track-on from surrounding integrated
surfaces and mechanical abrasion of the
treated road surface. Accordingly, each
value of control efficiency contained in
this report includes the time after
application that the measurement was
taken.
Two control techniques utilized to
reduce emissions from heavy-duty traffic
on unpaved roads were tested: (1) a 17%
-------
solution of Coherex® in water applied at
an intensity of 0.86 l/m2(0.19 gal./yd2),
and (2) water applied at an intensity of
0.59 l/m2 (0.13 gal./yd2). The control
efficiency for Coherex®, at the above
application intensity, averaged over the
first 48 hr after application, was 95.7%
for TP, 94.5% for IP, and 94.1% for FP. The
control efficiency for watering at the
above application intensity, 4.4 hr after
application, was 55.0% for TP, 49.6% for
IP, and 61.1% for FP. The control
efficiency of watering at the above
application intensity was above 95% for
all particle sizes half an hour after
application.
Only one control technique for emissions
from light-duty vehicles travelling on
unpaved roads was tested. The control
measure was a 17% solution of Coherex®
in water at an application intensity of
0.86 l/m2 (0.19 gal./yd2). The control
efficiency of Coherex® at the above
application intensity, 25 hr after applica-
tion, was 99.5% for TP, 98.6% for IP, and
97.4% for FP. This road had been closed
to traffic for a day. Fifty-one hours after
application, these efficiencies had decayed
to 93.7% for TP, 91.4% for IP, and 93.7%
for FP.
Three control techniques for mitigation
of emissions from vehicles travelling on
paved roads were tested: vacuum sweep-
ing, water flushing, and flushing with
broom sweeping. The highest measured
values for the control efficiency of
vacuum sweeping, occurring 2.8 hr after
vacuuming, were 69.8% for TP, 50.9%for
IP, and 49.2% for FP. The control
efficiency for water flushing at 2.2 l/m2
(0.48 gal./yd2), about 40 min after
application, was 54.1 % for TP, 48.8% for
IP, and 68.1% for FP. The control
efficiency for flushing and broom sweep-
ing, about 40 min after application with
water applied at 2.2 l/m2 (0.48 gal./yd2),
was 69.3% for TP, 78.0% for IP, and 71.8%
for FP.
Earlier MRI studies of open dust
sources in the iron and steel industry
produced data bases which were used to
develop predictive emission factor equa-
tions. The precision factors for the paved
and unpaved road equations were 2.20
and 1.48, respectively. When the results
of the 18 tests of uncontrolled particulate
emissions from vehicular traffic on roads
performed during this study were added
to the data bases, the precision factors
increased to 3.95 and 1.98, respectively.
These increases indicate the need for
possible refinement of the paved and
unpaved road equations based on the
'arger data bases now available.
A portable wind tunnel was used to
measure uncontrolled and controlled
emission factors from storage pile wind
erosion. The wind tunnel involves measur-
ing the amount of emissions eroded from
a given surface under a known wind
speed. MRI's portable open-floored wind
tunnel was placed directly on the surface
to be tested, and its wind flow adjusted to
predetermined centerline speeds. The
emissions eroded from the surface were
measured isokinetically at a single point
in the sampling section of the tunnel with
a sampling train consisting of a tapered
probe, cyclone precollector, parallel slot
cascade impactor, backup filter, and high
volume sampler.
Wind erosion from storage piles was
quantified during 29 tests of uncontrolled
and controlled emission factors. Nearly
all of the tests were on coal surfaces with
two control techniques studied separately:
(1) a 17% solution of Coherex® in water
applied at an intensity of 3.4 l/m2 (0.74
gal./yd2), and (2) a 2.8% solution of Dow
Chemical M-167 latex binder in water
applied at an average intensity of 6.8 l/m2
(1.5 gal./yd2). The control efficiency of
Coherex® applied at the above intensity
to an undisturbed steam coal surface
approximately 60 days before the test,
under a wind of 15.0 m/s (33.8 mph) at
15.2 cm (6 in.) above the ground, was
89.6% for TP and about 62% for IP and FP.
The control efficiency of the latex binder
on a low volatility coking coal 2 days after
application, under a 14.3 m/s (32.0 mph)
wind speed at 15.2 cm (6 in.) above the
ground, was 37.0% for TP and near zero
for IP and FP. However, when the wind
speed was increased to 17.2 m/s (38.5
mph), the control efficiency increased to
90.0% for TP, 68.8% for IP, and 14.7% for
FP. The efficiency under the same wind
speed, 17.2 m/s, decayed 4 days after
application to 43.2% for TP, 48.1 % for IP,
and 30.4% for FP.
Three iron and steel plants were
surveyed to determine open source
emission control design, operation, and
cost parameters. Design and operation
parameters included application intensity
and frequency, life expectancy, applicator
equipment manufacturer, normal operat-
ing speed, capacity, fuel consumption,
vehicle weight, number and capacity of
nozzles at a specified pressure, and
maintenance problems. Cost data included
operating, maintenance, and capital invest-
ment costs. The operating and maintenance
costs were further divided into labor,
gasoline and oil, maintenance and repair,
and depreciation costs. The capital
investment costs included purchase and
installation of primary and ancillary
equipment.
Conclusions from this study are:
1. Open dust emissions from the entire
integrated iron and steel industry for
1978 were estimated at 88,800 T/yr
of suspended particulate. The total can
be subdivided into general categories:
Category
Percent Contribution
Vehicular traffic 70.4
on unpaved roads
Wind erosion 15.0
Vehicular traffic 12.7
on paved roads
Continuous raw material 1.6
handling operations
Batch raw material 0.3
handling operations
2. A decay in control efficiency with time
after application was measured for
most of the control techniques tested.
This means that a reported efficiency
value has meaning only when given in
conjunction with a time after a specified
application. Within 5 hr of application,
the control efficiency afforded by
watering of unpaved roads decayed
from nearly 100% to about 60%, but
the control efficiency of Coherex®
remained above 90% over the first 2
days after application. The decay rates
of control measures applied to paved
roads (which were much less effective
than those applied to unpaved roads)
were high; i.e., comparable to the rate
observed for watering of unpaved
roads.
3. There is some indication that short-
term control efficiency varies as a
function of particle size, especially for
the paved road control techniques
tested. For example, vacuuming is less
effective in controlling fine particle
emissions, but the opposite is indicated
for water flushing.
4. Wind erosion from the coarse aggre-
gate storage piles tested and observed
at iron and steel plants is probably
much less than previously thought.
Tests show that, for typical storage pile
surfaces, 10m wind speeds in excess
of 14.8 m/s (33.2 mph) are necessary
for wind erosion to even begin. Also,
crusts on piles and exposed surfaces
are very effective inhibitors of wind
erosion as long as the crust remains
unbroken. Current thinking suggests
that the major wind erosion problem
exists on unencrusted areas (e.g.,
surrounding the piles, exposed areas,
and road shoulders) and unpaved
roads. Also, piles which have dozer or
scraper traffic on them (atypical in the
-------
iron and steel industry) are susceptible
to wind erosion. Finally, as would be
expected, uncrusted piles of fine dry
material are also susceptible to wind
erosion.
5. The control efficiency of the latex
binder tested for effectiveness in
reducing wind erosion increased with
increasing wind speeds. This may
apply to other wind erosion dust
suppressants and to a broader range of
wind speeds than those tested, but the
data are still too sparse to support that
inference.
6. The optimal cost-effective technique
for applying open dust controls is to
make the application and then reapply
only after the initial application has
decayed to zero control efficiency.
However, this will yield only about
50% control efficiency, assuming the
technique started at 100%. In controlled
emissions trading (such as offsets,
banking, and bubbles), much more
than 50% reduction in open dust
source emissions may be needed.
Thus, optimization of cost-effective-
ness in the control of open dust source
emissions must always be considered
in the context of a minimally acceptable
level of control.
There is no clear-cut definition of
"best" control strategy for open dust
source emissions. Two possible defini-
tions are:
a. That strategy which achieves the
constraint of an acceptable level
of emissions reduction at the
least cost; and
b. That strategy which achieves the
minimally acceptable level of
control and is the least expensive
per unit mass of emissions
reduced.
Although the cost of (b) cannot be
less than that of (a), (b) may indeed
prove to be more desirable in the
long term because greater offsets
are possible and thus represents the
most efficient use of funds.
7. Evaluation of the emission reduction
effectiveness of an open dust
source control measure requires
the acquisition of detailed perfor-
mance data on the control measure.
The performance data gathered to
date on open dust sources in the
iron and steel industry have focused
on the efficiences of freshly applied
control measures for given sets of
application parameters. Additional
field tests would be required to
determine the long term efficiency
decay.
8. As with the initial control efficiency,
the decay rate of a control measure
should depend in part on the
application parameters. Taking
unpaved roads as an example, the
frequency of application, the appli-
cation intensity, and the dilution
ratio of the chemical suppressant
are of paramount importance. Also,
there may be a residual effect of
previous control applications which
changes the shape of the decay
curve, although this residual effect
may become less important after
repeated reapplication/decay cycles.
Theoretically, a mathematical rela-
tionship could be developed which
expresses mean control efficiency
(during the period between applica-
tions) as a function of the application
parameters and the frequency of
application once a sufficiently
large emissions data base has been
obtained.
9. As part of the emission trading
process, a calculated emission
reduction requires information on
the uncontrolled emission factor
and the performance of the proposed
control measure. Except for unpaved
roads, the current uncontrolled
open source emission factor equa-
tions listed in Table 1 are based on a
limited number of tests. The control
efficiency data base for these
sources is even more limited, both
in the small number of control
efficiency values measured and the
lack of data on the long-term
efficiency of controls. This situation
leads to corresponding levels of
uncertainty when implementing
emission trades.
Metric Conversion Factors
Readers more familiar with metric
units may use the factors below to
convert the English units used in this
summary.
English
Multiplied by Metric
acre
ft
gal./yd2
Ib
Ib/acre year
Ib/ton
Ib/vehicle mile
mile
mph
ton
0.00405
0.305
4.53
0.454
J12
0.500
0.282
J.6J
0.447
0.907
0.765
km2
m
liters/m2
kg
kg/km2 year
kg/tonne
kg/vehicle km
km
m/s
tonne
m3
T. Cuscino, Jr., G. E. Muleski. and C. Cowherd. Jr., are with Midwest Research
Institute. Kansas City, MO 64110.
Robert C. McCrillis is the EPA Project Officer (see below).
The complete report, entitled "Iron and Steel Plant Open Source Fugitive Emission
Control Evaluation." (Order No. PB 84-110 568; Cost: $17.50, subject to
change) will be available only from:
National Technical Information Service
5285 Port Royal Road
Springfield, VA22161
Telephone: 703-487-4650
The EPA Project Officer can be contacted at:
Industrial Environmental Research Laboratory
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
-------
United States
Environmental Protection
Agency
Center for Environmental Research
Information
Cincinnati OH 45268
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