FUGITIVE DUST FROM MINING OPERATIONS
Final Report
Contract No. 68-02-1320
Task No. 6
by
T. R. Blackwood
T. F. Boyle
T. L. Peltier
E. C. Eimutis
D. L. Zanders
Prepared for
Control Systems Laboratory
U.S. Environmental Protection Agency
Research Triangle Park, North Carolina 27711
Date Prepared: May 1975
MONSANTO RESEARCH CORPORATION
DAYTON LABORATORY
DAYTON, OHIO 45407
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TABLE OP CONTENTS
ABSTRACT
I. INTRODUCTION I
II. BACKGROUND 3
A. Definition of Sources Considered 3
B. Criteria for Ranking Emissions Due to l\
Fugitive Dusts
III. LITERATURE SEARCH 7
A. Hazard Potential of Emissions 7
B. Population Proximity to Fugitive Dusts 9
C. Mass Emissions 10
IV. PRELIMINARY RANKING OF SOURCES 1X
A. Largest Sites H
B. Ranking of Sources by Industry ]_]_
C. Selection of Sampling Sites 12
V. PREPARATION OF PROCEDURES 13
A. Atmospheric Dispersion Modeling . 13
B. Field Sampling and Analytical Methodology 14
VI. FIELD TESTS 16
A. Data Summary 16
B. Error Analysis 16
C. Analysis of Data 17
VII. TABLES AND FIGURES 19
VIII. REFERENCES 2J2
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ABSTRACT
This study is intended to generate preliminary data on the
physical and chemical nature.of fugitive particulates emitted
from the processing of ores. This information will be used
to supply a base for further studies on open source emissions.
A method for relating the potential hazard to public health
of 32 mining and mineral handling industries is presented.
These are ranked to reflect potential hazard due to the type of
operation. Six sources were selected for sampling out of four
industries. The sources are: Sand and Gravel Sizing and
Crushing, Coal Storage, Loading of Dried Phosphate Rock,
Storage of Phosphate Rock, Storage of Kaolin Clay in Silos,
and Kaolin Processing Plant Surroundings.
Emission factors have been derived for each of these source
types. A method for obtaining these factors from the use of
dispersion equations and ambient sampling is also described.
Sampling and analytical methodology is given in a separate
report under Task 10 of EPA contract 68-02-1320 entitled
"Fugitive Dust from Mining Operations - Appendix."
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SECTION I
INTRODUCTION
The objective of this project is to provide preliminary data
necessary for future planning of EPA action in developing
control technology for open sources of mining operations. A
model was developed which served as the basis for ranking
various open sources of fugitive dust emissions. The criteria
for this model emphasized the probable impact of emissions on
public health.
Factors considered in establishing this were:
1. Open source mass emissions
2. Proximity of operation to population
3. Toxicological character of the dust (chemical)
4. Respirable hazard potential
Using this criteria^ a ranking of source types was made based
upon the ore or mineral handled. Indirect impact on public
health (vegetation damage, etc.) is not emphasized in this study,
Prom this ranking of sources3 the top ten source types were
selected and recommended for testing. Some deviation from the
ranking order did occur in the site selection. Sources were
chosen for testing which presented a representative case but
with the least interferences from other sources.
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Presurveys of several sites were conducted and the field
sampling effort planned. Concurrent to the presurvey,
atmospheric dispersion modeling for data reduction and
laboratory analysis effort planning was completed.
The field sampling and laboratory analysis effort was carried
out under a separate contract and the results reported herein.
Of the ten selected sources, four were tested and evaluated
and one was eliminated due to a drastic reduction in U.S.
activity since 1971.
Work was discontinued on this contract when it was determined
that a more comprehensive study was needed of all types of open
sources. The overview and methodology developed for use in
this work was used to provide a perspective to more detailed
investigations of open sources.
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SECTION II
BACKGROUND
A. DEFINITION OP SOURCES CONSIDERED
The characteristics of particulate emissions to the air from
mining and mineral handling operations is too general a subject
for complete investigation of all factors. Those sources which
present the greatest detriment to public health are of concern.
Logically, certain industries and mineral handling operations
may be excluded from this and practical considerations. In
addition, those facets of industry which are currently recognized
as point sources and are under control guidelines should not be
considered.' An example is the curing of phosphate rock. Over
90$ of the industry uses collection equipment and is subject to
improving the present level of control.1 These sources are
controlled and governed by local, state and federal investigatory
agencies. Furthermore, in-plant emissions, which are controlled
by OSHA, shall be excluded from study for similar reasons,
"Public health" in this study shall be confined to effects on
non-employees. Ores which contain nuclear energy are likewise
excluded since the AEG carefully regulates the handling of these
products. Also, the effect on "public health" with nuclear
products is routinely non-existent.2
For the scope of this project, consideration was given to the
following:
1. Primary particulate emissions in air
2. Open mining of ores
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3. Beneficiation prior to ore shipment to refinery or
smelter
4. Materials handling of import ores and ore concentrates
(excludes transport)
5. Stockpiling of minerals (active piles only)
These sources shall be analyzed for hazard potential on the
basis of the primary emissions. Synergistic and secondary
effects have not been identified nor characterized in this study.
B. CRITERIA FOR RANKING EMISSIONS DUE TO FUGITIVE DUSTS
This criteria has been developed to determine the relative hazard
potential of fugitive dust emissions from mining and mineral
handling operations where the source emissions are known. It is
intended that it be used to compare situations which are of
potential public health hazard.
There are three major factors in defining this hazard potential
for a source which are as follows:
E - Respirable dust emissions from source; either by
estimate or measurement. Examples are:
a. Production data times an emission factor.
b. Losses in storage from formula or measurement.
M - Relative toxicity of the emissions.
D - Distance to population centers.
The formula postulated to represent the relative hazard potential
of a source is as follows:
M XSD2 = Relative Hazard (1)
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The M (in the formula) will be the MAAC calculated from the
following equation:
MAAC = TLV®x 23.8 (2)
where:
MAAC = Maximum allowable ambient concentration (tug/m3)
TLV® = The Lowest Threshold Limiting Value of the
three defined below (yg/m3)
TLV's® could be determined in one of three ways by relating
chemical, fibrous or silica toxicity information.
Since chemical TLV's® are for specific minerals or compounds,
the composition of the dust is important. A composite TLV®
may be developed by multiplying the individual TLV® by the
fractional composition of its source. When fibers are present
they may be compared by using the conversion:
TLV®(fibers/ml) x 1.33 = TLV®(chemical) (3)
For silica, the quartz content is used to determine a TLV® for
the total dust. This is as follows:
TLV®= 22. (4)
% Quartz + 3
% Quartz by x-ray diffraction
Composite TLV's® are calculated as follows:3
TLV® = (5)
n f
I i
1=1 TLV,
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where
f. = fraction of particulate as substance i
TLV.®= TLV® of substance i
n = total number of substances in particulate
so that n
I f. = 1.0
1=1 x
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SECTION III
LITERATURE SEARCH
One of the major objectives of the literature search was to
quantify the three factors used to derive the hazard equation
defined in section II-B. The search resulted in the following
findings.
A. HAZARD POTENTIAL OF EMISSIONS
A search of the literature for toxicological character of various
dusts was instigated and the general indications were quite
surprising. In the case of mining operations, health hazards
are associated with the gangue as opposed to the mineral or
metal. The largest danger is due to quartz and silicon based
compounds. The presence of metal oxides also tends to aggravate
this situation, lowering the threshold limiting value (TLV)®.
Very little data exists in the literature on toxicity of ores
or their concentrates. Seventy eight minerals, metals and
non-metals were searched and Table 1 is a summary of the substances
for which data is available.
For mineral handling, identification of the hazard is more
complex especially in terms of site identification. An example
is the iron ore storage area for loading lake shippers at
Escanaba. This site has good control on all transfer and
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conveying points. However, the dust deposited on the city is
heavy and apparently comes from the storage piles. Observers
contacted indicated that dust leaving the piles is not usually
at a visible level.
From the literature search we find that the chemical" form of the
dust is very important to hazard potential. Common ores and
pure minerals are generally believed to be non-toxic. In mining,
the gangue is more of a problem due chiefly to silica content.
With mineral handling, silica is always a possible hazard but
more important is usually the chemical or fibrous toxicity, since
the ore has usually been beneficiated. For these comparisons,
we selected the TLV® for workroom air taking into account the
usual forms of the mineral and then selecting the lower TLV®.
This gives a worse case (yet realistic) analysis of toxicity
for use in hazard relationships. This allows the use of ele-
mental analysis to compare toxicity and thus hazard potential.
The TLV's® listed in Table 2 can also be converted to maximum
allowable ambient concentrations (MAAC) or maximum allowable
ore concentrations (MAOC). The maximum allowable ore concen-
tration is defined as the maximum percentage of a pollutant
which can exist as a component in the ore and still permit the
ore to be treated as inert material. If the fugitive dust
contains less than the MAOC, then it is logical to consider it
as inert and the MAAC for inert dust applies. If the dust
contains more than the MAOC then that mineral's MAAC dominates.
For example: A sample is analyzed and the results are as
follows:
Concentration: 100 pg/m3
Analysis: Zinc 50$ 50 yg/m3
Aluminum 40$ 40 yg/m3
Lead 10% 10 yg/m3
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Since the MAOC of lead is exceeded, its MAAC applies in this case
(4.76 vig/m3). Thus M in equation (1) would be 4.76. For com-
parison purposes, the relative toxicity of each component can
be used (relative toxicity being the ratio of the % MAOC to the
% of the element in the dust).
Zinc 1.0
Aluminum 0.4
Lead 5.0
If lead were found as 2% or less, and copper comprised the other
8%, then this dust would be considered inert and the MAAC for
inert dust would then apply (238 yg/m3)*.
B. POPULATION PROXIMITY TO FUGITIVE DUSTS
Population densities and the proximity of the emission sources
to population centers are both needed in evaluating the effect
of emissions on public health. None of this data was readily
available in the literature. For the most part the data did
not exist and where it was available, it was not in a form
usable in the hazard equation. In addition, because of the
large number of sources being considered, it was determined that
it would be beyond the scope of the contract to attempt to
generate the basic population data.
As a result of this lack of information another approach was
used to estimate the impact on public health and is discussed
in section IV-B (p. 11).
* The term "inert" is quite often confused with "non-hazardous"
This is not the case; it only implies that a specific chemical
or fibrous toxicity is lacking.
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C. MASS EMISSIONS
Data relating to the emission rates of the different types
of sources was very sketchy. While the results of direct source
sampling would be used in the final ranking of emission sources,
information from the literature was needed to perform the pre-
liminary ranking necessary to help direct the sampling program.
The preliminary ranking was accomplished by assuming that the
emission rate is proportional to the mass of material handled
and assuming a constant emission rate for all industries. Thus,
U.S. production rates could be used to rank the industry types.
While this approach is crude, it nevertheless provides a basis
for preliminary ranking of source types.
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SECTION IV
PRELIMINARY RANKING OP SOURCES
A. LARGEST SITES
Initially, a listing was made of the mines having the largest
production of ore in the country. One list was made for both
metal and non-metal mining (Tables 3 and 4 respectively).
When it was found that the leading mining operations were
composed of relatively few industries, the lists were modified
to include other industries for consideration. The largest
sites from each were selected for possible consideration. The
resulting list is presented in Table 5-
B. RANKING OP SOURCES BY INDUSTRY
To arrive at a preliminary ranking of sources the industries
were ranked by mining production and then re-ranked with
consideration to population affected. The annual U.S. production
of minerals is given in Table 6 for 32 industry segments. A
comparison of relative hazard is obtained by dividing the U.S.
production of a mineral by the mineral's TLV®. In Table 7,
these were further reduced to ten by applying the following
questions:
1. Are these sources usually near population centers?
2. What is the mineral hazard potential most likely to be?
3. As a respirable hazard how do they rank (only particles
less than 5v and fibers are defined as hazards).
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C. SELECTION OF SAMPLING SITES
From the ten industries we selected one site from each to sample.
More than one candidate was usually available in a geographical
area which allowed the selection of the most favorable sampling
conditions at that location. In addition, Special interest
sites were selected to evaluate fugitive dust from slagging and
smelter wastes. For selection of sampling sites, the flat-lands
were preferred over other sources. Practical considerations of
weather conditions also influenced greatly the order of sampling.
The criteria used in the selection of the test sites are as
follows:
1. Elimination of process emissions which are in the
implementation phases of control.
2. A minimum of other industry which could cause a
substantial change in the background level.
3. If possible, sources near large population centers
or sources which have received complaints.
We were able to work with the industries identified as potential
hazards in our sampling efforts. Working on the property of the
source owner expedited the sampling effort by reducing the amount
of time needed to obtain reasonable loadings on the filters.
We also had better control on estimating the plants operation
compared to the year-round conditions.
One industry of the ten selected was eliminated. It was found
that production of mercury has dropped to about 4$ of that
reported in 1971. Thus mercury is about 30th on the list
(Table 6) and was no longer a factor in our study.
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SECTION V
PREPARATION OF PROCEDURES
A. ATMOSPHERIC DISPERSION MODELING
For the arrangement shown in Figure 1, let the origin be defined
as the source and all remaining points in the usual Cartesian
coordinate system. Let 0 be the angle of mean wind direction.
Then to find the downwind distance of any point y. perpendicular
to the wind direction centerline we compute the following:
ml = tan 0
and for point S. with coordinates x., y.
m2 = 5E7
the angle a is found from
a = arctan ^ . „ —
1 + Hi} • ni2
the lateral distance Y. is:
Y. = (sin a)
and the downwind distance X. is
X± = (cos a) VXi:i + yi
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Estimates of stability class were determined using.the
procedure described in Figure 2. The dispersion coefficients
0 and 0 can be estimated from Figures 3-2 and 3-3 of Turner's
"Workbook of Atmospheric Dispersion Estimates".4 The source
strength Q will be calculated as an average of the calculations
done for N sampler readings using the following equation:
Q = E
n X. TT 0y
1=1 N exp -
f-1/2 Yi
L v
where: u = average wind speed
This process was computerized for efficient reduction of
experimental data.
B. FIELD SAMPLING AND ANALYTICAL METHODOLOGY
A Gaussian plume equation was used to estimate the fugitive dust
source strength from ground level ambient air samplers. A
typical arrangement is shown in Figure 3. The dispersion coef-
ficients were calculated using a stability class determination
such as shown in Figure 2. Since the Gaussian plume equation
gives a time averaged description of the concentration field, a
number of receptors-were used as shown in Figure 3. The following
rationale applies: (1) The position of the sampler determines
ambient dust levels. (2) Wind direction and velocity were not
constant and affect time-averaging results. Our positioning
of samplers eliminated the need to correct for time varying
concentrations.
Sampler 1 is the primary source strength estimator and was
located as close to the source as possible. This sampler was
placed from one to three obstruction heights from the source
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depending upon stability class. Sampler 3 is required for
correlations with downwind power law decay. Samplers 2 and 4
are required for correlations with lateral dispersion estimates
and to estimate how close we are to the plume centerline.
Sampler 0 was used as a blank to compensate for other dust
sources upwind of the site under investigation.
A portable mechanical meteorological station gave the remaining
parameter, wind speed. The azimuth and speed were monitored
throughout the sampling period. Analytical data was gathered
from the laboratory scheme shown in Figure 4. By identifying the
hazardous materials in the source dust, subsequent analysis
expense was minimized. Figure 5 is a pictoral overview of the
integrated sampling and analytical scheme. The filter from
Sampler 4 was analyzed by X-ray florescence techniques and
compared to source materials for composition. A particle count
was also made via optical microscopy to check for fibers and to
give a different perspective to particle size distribution.
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SECTION VI
FIELD TESTS
A. DATA SUMMARY
The results of the field sampling program have been summarized
according to the composition of the samples (Table 8) and the
emission rates, emission factors and particle size distributions
(Table 9).
A number of clarifications are .necessary for Table 9. In the
'Comments' column of the table, notes were made when the wind
speed averaged less than 3 mph. It has been found that 3 mph
is usually the minimum speed at which a good sample can be
obtained. Also in the comments column is a note regarding wind
change. Excessive wind shift has been defined, through
experience, as a change in wind direction greater than 20°.
The notes made in the phosphate rock and Kaolin Processing
section of Table 9 show which operation of the processing
was being sampled.
B. ERROR ANALYSIS
Standard deviations and 95% confidence limits were calculated for
each emission rate (Table 10). The confidence limits were calcu-
lated from the following equation:
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L = . g 95
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a significant length of the sampling period. This will result
in a large standard deviation. This then gives a representation
of the degree of variation of the source emissions.
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SECTION VII
TABLES AND FIGURES
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Table 1. TOXICITY SEARCH
Silica - Quartg: dust and ores
Barium - Minerals and ores
Graphite - Minerals and ores
Beryllium - Minerals and ores
Phosphate - Rocks and ores
Potassium - Ores and minerals
Sulfur - Ores and minerals
Molybdenum - Ores and minerals
Cadmium - Ores, minerals and oxide
Coal
Gold - Ores
Antimony - Ores and minerals
Titanium- Ores and minerals .
Talc - Ores and dusts
Tungten - Ores
Iron - Ores and minerals
Manganese - Ores
Kaolin - Ores
Bauxite and Alumlum oxide
Chromium .- Ores
Arsenic - Ores and minerals
Zinc - Ores and minerals
Nickel - Ores
Tellurium - Ores and minerals
Copper - Ores and minerals
Fluorspar
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Table 2. TOXICITY MEASURES
TLV
MAAC
Beryllium
Silver
Mercury
Silica
Tellurium
Cadmium (oxides)
Lead
Selenium
Uranium
Antimony
Arsenic
Barium (Barite)
Chromite ores
Copper
Iron salts
(soluble )
Nickel
Coal
Sulfur
Tin
Carbon Black
(Graphite )
Lime
Manganese
Molybdenum
Zinc
Tungsten
Asbestos
Talc
(non fiber)
(fibrous - Tremolite)
Inert dusts
3
mg/m
0.002
0.01
0.01 (0.05 - metal)
30
Q + 3
Q = % quartz
(x-ray diffraction)
0.1
0.2
0.2
0.2
0.2
0.5
0.5
0.5
0.5 (1.0 for
insol. metals)
1.0
1.0
1.0
2.0
2.0
2.0
3.5
5.0
5.0
5.0 (10.0 for insoluble)
5.0 (1.0 for cl)
6.0
5 fibers/ml>5p
20 mppcf
5 fibers/ml>5p
10 (30 mppcf)
3
yg/m
0.0*48
0.24
0.24
714
Q + 3
2.38
4.76
4.76
4.76
4.76
11.9
11.9
11.9
11.9
23.8
23.8
23.8
47.6
47.6
47.6
83.4
119.
119.
119.
119.
143.
159.
238.
MAQC
ppm
20
100
100
(*)
1
2
2
2
2
5
5
5
5
10
10
10
20
20
20
35
50
50
50
50
60
Fluorspar, Titanium, Aluminum, Boron, Iron (insoluble), Kaolin, Phosphate and
and Gypsum are "inert".
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Table 3. LARGEST METAL MINING OPERATIONS5
(Ranked by Size)
Ho.
1.
2.
3.
H.
5.
6.
7.
8.
9.
10.
11.
12
13.
in.
15.
16.
17-
18.
19.
20.
21.
22.
23.
24.
25.
Mine
Utah Copper
Slerrita
Hoyt Lake
Peter
Mitchell
Morencl
Berkeley Pit
Minntac
Plma
Eagle
Mountain
Twin Buttes
Ray Pit
Questa
Tyrone
New Cornelia
Yerington
Chino
San Manuel
Climax
Empire
Inspiration
Butler
Republic
National
Steel
White Pine
Ruth
Operator
Kennecott Copper Corp.
Duval Sierrita Corp.
Pickands Mather & Co.
Reserve Mining Co.
Phelps Dodge Corp.
The Anaconda Company
U. S. Steel Corp.
Pima Mining Co.
Kaiser Steel Corp.
The Anaconda Company
Kennecott Copper Corp.
Molybdenum Corp. of
America
Phelps Dodge Corp.
Phelps Dodge Corp.
The , naconda Company
Kennecott Copper Corp.
Magma Copper Co.
American Metal Climax,
Inc .
Cleveland-Cliffs Iron Co.
Inspiration Consolidated
Copper Co.
The Hanna Mining Co.
Cleveland-Cliffs Iron Co.
The Hanna Mining Co. •
White Pine Copper Co.
Kennecott Copper Corp.
Commodity
Copper
Copper
Iron ore
Iron ore
Copper
Copper
Iron ore
Copper
Iron ore
Copper
Copper
Molybdenum
. Copper
Copper
Copper
Copper
Copper
Molybdenum
Iron ore
Copper
Iron ore
Iron ore
Iron ore
Copper
Copper
State
Utah
Ariz.
Minn.
Minn.
Ariz.
Mont.
Minn.
Ariz.
Calif.
Ariz.
Ariz.
N. Mex.
N. Mex.
Ariz.
Nev.
N. Mex.
Ariz.
Colo.
Mich.
Ariz.
.Minn.'
Mich.
Minn.
Mich.
Nev.
County
Salt Lake
Pima
Itasco
St. Louis
Cochise
Silver Bow
St. Louis
Pima
Riverside
Pima
Final
Taos
Grant
Pima
Lyon
Grant
Final
Clear Creek
Marquette
Gila
Itasco
Marquette
Itasco/
St. Louis
Ontonagon
White Pine
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Table 4. LARGEST NON-METAL MINING OPERATIONS5
(Ranked by Size)
No.
1.
2.
3.
1).
5.
6.
7.
8.
9.
10.
1.1.
12.
13.
11.
15-
16.
17.
18.
19-
20.
22.
23.
21.
25.
26.
27.
28.
29.
Mine
Payne Creek
Kingsford
Ft. ' Meade
Suwannee
Noralyn
Haynsworth
Rockland
Palmetto
Saddle Creek
Bonny Lake
Clear Spring
Silver City
Tampa
Agricultural
Chemical
Operation
Tenoroc
Boron
Lee Creek
Watson
International
Bartow
Retsof
PCA
Gay
Hobbs
Carlsbad
Leefe
Westvaco
Nichols
St. Mary
Henry
Operator
Continental Oil Co.
International Minerals
& Chemical Co.
Mobil Oil Co.
Occidental Petroleum
Corp.
International Minerals
& Chemical Co.
Brewster Phosphate
U.S.S. Agri-Chemicals,
Inc.
Continental Oil Co.
Continental Oil Co.
W.R. Grace & Co.
International Minerals
Swift Agricultural
Cities Service Co.
Borden, Inc.
U.S. Borax & Chemical
Corp.'
Texas Gulf Inc.
Swift Agricultural
Chemicals Corp.
International
Minerals &
Chemical Co.
U.S.S. Agri-Chemical,
Corp.
International Salt Co.
Potash Co. of America
J.R. Simplot Co.
Kerr-McGee Chemical
AMAX Chemical Corp,
Stauffer Chemical
FMC Corp.
Mobil Oil Corp.
Carglll Inc.
Monsanto Co.
Commodity
Phos rock
Phos rock
Phos rock
Phos rock
Phos rock
Phos rock
Phos rock
Phos rock
Phos rock
Phos. rock
Phos rock
Phos rock
Phos rock
Phos rock
Boron
Phos rock
Phos rock
Potassium
Phos rock
Salt
Potassium
Phos rock
Potassium
Potassium
Phos rock
Sodium
Phos rock
Salt
Phos rock
State
Fla.
Fla.
Fla.
Fla.
Fla.
Fla.
Fla.
Fla.
Fla.
Fla.
Fla.
Fla.
Fla.
Fla.
Calif.
N.C.
Fla.
N.Mex.
Fla.
N. Y.
N.Mex
Idaho
N.Mex.
N.Mex.
Wyo.
Wyo.
Fla.
La".
Idaho
County
Polk
Polk
Polk
Hamilton
Polk
Polk
Polk
Polk
Polk
Polk
Polk
Polk
Hillsbough
Manatee
Inyo/Kern
Beaufort
Polk
Eddy
Polk
Schuyler
Eddy
Bingham
Lea
Eddy
Lincoln
Sweatwater
Polk
St. Mary
Caribou
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Mine
Table 5. LARGEST MINING OPERATIONS5
Operator State Countj
Iron
Hoyt Lake
Peter Mitchell
Minntac
Molybdenum
Questa
Climax
Phosphate rock
Payne Creeks
Kingsford
Ft. Meade
Suwannee
Boron
Boron
Nickel
Riddle
Kennecott
Duval Sierrlta
Phelps Dodge
Anaconda
Pickand Mather
Reserve Mining
U.S. Steel
Molybdenum Corp.
of America
American Metal Climax
Continental Oil
IMC
Mobil Oil
Occidental Petroleum
U.S. Borax + Chemical
Hanna Mining
Tungsteln (usual coproduct of Molybdenum)
Pine Creek Union Carbide
Leadville Amax
Aluminium (Bauxite)
American Cyanamid
Alcoa
Reynolds
Utah
Arizona
Arizona
Montana
Minn.
Minn.
Minn.
N. Mex.
Colorado
Florida
Florida
Florida
Florida
California
Oregon
California
Colorado
Arkansas
Arkansas
Arkansas
Salt Lake
Pima
Cochise
Silver Bow
Itasca
St. Louis
St. Louis
Taos
Clean Creek
Polk
Polk
Polk
Hamilton
Inyo/Kern
Douglas
Inyo
Clear Creek
Pulaski/Saline
Saline
Saline
MONSANTO RESEARCH CORPORATION
-------
Table 5 cont.
Antimony
Sunshine Mine
Sunshine
Arsenic (Byproduct recovery)
Asarco
Beryllium and Fluorspar (mined together)
Brush Willman
Spor Bros.
Lead and Bismuth (mined together)
Idaho
Wash
Utah
Utah
Tellurium (from Zinc)
International Smelt &
Refinery
Asarco
International Smelt &
Refinery
New Jersey
Maryland
New Jersey
Shoshone
Pierce
Juab/Millord
Juab
(•smelter)
Mine only
(smelter)
Mercury
Selenium (from
Asarco
Missouri Lead Operation
(Amax and Homestake)
Asarco
Bunker Hill
Buttes gas and oil
Lansdowne Mining
One-Shot Mining
Star City
copper)
Kennecott
Asarco
Texas
Missouri
Montana
Idaho
California
California
California
California
Utah
Maryland
El Paso
Iron
Lewis and Clark
Shoshone
Marin
Napa
Napa
Pershing
Garfield
Baltimore
Middlesex
Baltimore
Middlesex
Zinc (Cadmium, Germanium, Indium, Thallium, Gallium produced also)
American Zinc Tenn. Jefferson/Knox
Tenn.
(refined only)
New Jersey Zinc
Asarco
Tex.
Hancocks/
Jefferson
El Paso
• MONSANTO RESEARCH CORPORATION
-------
Table 5 cont
Anaconda
Perlite, Pumice. Vermiculite (volcanic ash)
John Mansville
LaBue Axtell
W.R. Grace and Co.
Asbestos
Cement (suggested)
Talc
(pyrophyllite)
Atlas Asbestos
Coalinga Asbestos
Union Carbide
Ash Grove Cement
Univ. Atlas
Hartford Talc
H.N. Stewart
Piedmont Minerals
Montana
California
California
California
Kansas
Kansas
Maryland
Nevada
N.Carolina
Cascade
N. Mex.
Nebr.
Montana
Taos
Lincoln
Lincoln
Fresno
Fresno
San Benlto
Neosho
Montgomery
Harford
Esmeralda
Orange
26
• MONSANTO RESEARCH CORPORATION •
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Table 6. PRODUCTION
Industry
USP (Tons)0
TLV® RATIOS
USP
TLV
Rank
Coal storage
Sand and
gravel
Limestone
(70% of stone)
Borax
Iron
Bentonite
and clay
minerals
Lead
Trona and
Brine recovery
Copper
Barlte
Gypsum
Phosphate
rock
Pumice
Zinc
Talc
Silver
Lithium
Feldspar
Mercury
Uranium
Dlatoralte
Bauxite
Vermlcullte
Asbestos
(abrasives)
Sillimanlte,
Andaluslte,
Kyanlte
Nickel
Arsenic
Flurospar
Molybdenum
Beryllium
Rare earths
and Gems
557,000,000
918,000,000
571,200,000
168,873,000
90, 453,000*
57,233,000
573,000"
17,617,000
1,170,815*
525,000
10,000,000
5,605,000
3,530,000
852,000*
958,000
1,300*
2,900
718,000
670*
12,907"
627,000
418,000
289,997
120,690
139,000
13,073*
6,100.
118,000
54,796*
7
11,400
278,000,000
91,800,000.
57,120,000
16,887,000
9.0U5.000
5,723,000
2 ,865,000
1,761,700
1,1470,815
1,050,000
1,000,000
560,000
353,000
170,400
113,700
130,000
116,000
74,800
67,000
64,535
62,700
11,800
28,999
18,103
13,900
13,073
32,200
11,800
10,956
3,500
1,140
1
2
3
a
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Graphite
3,000
857
32
1968 U.S. Production CUSP) In Tons from U.S. Bureau of Mines Bulletin 650,
Mineral Facts and Problems, 1970 Edition
"1971 Production from American Metal Market, Metal Statistics, 1973
27
• MONSANTO RESEARCH CORPORATION «
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Industry
Coal
Sand and
gravel
Limestone
Borax
Iron
Clays
Lead
Trona
Copper
Barite
Gypsum
Phosphate
rock
Pumice
Zinc
Talc
Silver
Lithium
Feldspar
Mercury*
Table
USP
TLV
278,000,000
91,800,000
57,120,000
16,887,000
9,045,000
5,723,000
2,865,000
1,761,700
1,470,815
1,050,000
1,000,000
560,500
353,000
170,400
143,700
130,000
116,000
74,800
67,000
7- HAZARD RA
Located Near
Population
Yes
Yes
Yes
No
Yes
Yes
Yes
No
Yes
No
Yes
Yes
No
See
Yes
No
No
No
Yes
Hazard
Coal
Silica
Dust
Silica
Dust, Silica
Lead, Mercury
Copper, Lead,
Mercury
Dust
Respirable
Hazard Ranking
5
9
6
7
3
Talc & Silica
10
Mercury
*Mercury production has recently stopped in the U.S.
28
• MONSANTO RESEARCH CORPORATION •
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Table 8. SAMPLE COMPOSITIONS
Sand & Gravel
29
• MONSANTO RESEARCH CORPORATION •
Phosphorus
Sodium
Magnesium
Aluminum
Silicon
Sulfur
Potassium
Calcium
Titanium
Chlorine
Chromium
Manganese
Iron •
Fluorine
% Moisture
Coal Storage1
Run 1 Run 2
N.D.
5
<1
T
-
5 '
<1
2
N.D.
<1
N.D.
N.D.
1
N.D.
N.D.
1
Source
Sample Run 1
N.D.
6.5
<1 .25
T
6.0
43.5
13
.25
<1 2.54
2
T
30.8
• 33
<1 .04
T
T
2
N.D.
10.58
N.D.
.11
9-4
N.D.
N.D.
7.6
• 31
6.2
57.0
• 77
3.16
22.7
.1
.26
.05
.05
1.74
N.D.
Not
Analyzed
Kaolin Processing
Source
Sample Run 1 Run 2 Run 5
N.D.
T
T
50.2
47-5
N.D.
.04
T
2.0
N.D.
N.D.
N.D.
.2
N.D.
• 37
.04
.17
45.0
52.0
N.D.
.09
.02
2.0
N.D.
N.D.
N.D.
• 31
N.D.
N.D.
T
.21
42.4
55-6
N.D.
.07
.07
1.2
N.D.
N.D.
N.D.
.3
N.D.
N.D.
T
• 31
53-4
43-0
N.D.
.1
.1
2.0
N.D.
N.D.
N.D.
1.0
N.D.
Phosphate Rock Processing
Source
Sample Run 3 Run 5 Run 6
32.6
.40
.09
2.11
5.45
N.D.
.14
41.4
1.10
N.D.
N.D-
.11
2.47
3-82
25.5
N.D.
' .2
2.5
4.1
N.D.
.08
60.0
.16
N.D.
N.D.
N.D.
1.6
5.8
25.6
N.D.
.73
2.93
. 13-9
N.D.
.07
49.8
.15
N.D.
N.D.
N.D.
1.46
5-1
22.4
N.D.
• 75
3.19
5-3
N.D.
.11
59-5
.21
N.D.
N.D.
N.D.
2.13
6.4
.48
- These are in yg/cm2 - percentages are unavailable
N.D. - None Detected
T - Trace
Note: Compositions were calculated after drying
-------
Table 9- FUGITIVE DUST RESULTS
2
O
z
z
H
O
3
m
w
m
O
I
O
O
3J
1)
O
-I
O
Minutes
Run
Sand & Gravel
Coal Storage
Phosphorus Rock Processing
U)
a
Kaolin Processing
Emission Rate Std. Dev.
Run 1
Run 2
207
47
.8006 #/hrl ±.1215 #/hr
1.716 #/hr | ±.683 #/hr
Run 1
Run 2
295
237
.1020 #/hrl± .0689 #/hr
.1777 #/hr|±.1384 #/hr
Run 1
Run 2
Run 3
Run 4
Run 5
Run 6
232
265
350
320
245
239
169-3 #/hr[
N . G .* 1
82.16 #/hr ]
250.2 #/hr 1
55-6 #/hr |
126.6 #/hr 1
±67-6 #/hr
N.G.*
± 40.82 #/hr
±166.6 #/hr
±5-35 #/hr
±86.1 #/hr
Run 1
Run 2
Run 3
Run 4
Run 5
235
290
295
235
235
17-92 #/hr|
104.9 #/hr 1
3383 #/hr!
N.G.* 'l
84.26 #/hr |
.±0.15 #hr
±68.1 #/hr ,
±2.085 #/hr
N.G.*
±74.14 #/hr
Particle Size
Distribution
4.605?
Less than 7u
10.75$
Less than 5v
10.11!?
Less than 7y
Less than 7u
Emission
Factor
( .1075 #/T
} .229 #/T
(.00906 #/T
) .0158 #/T
.434 #/T
N.G.*
.210 #/T
. 639 #/T
.142 #/T
.322 #/T
2.39 #/r~
13.98 #/T
.452 rr
11.23 iK/T
Comments on Run
Wind speed <3 mph
Excessive wind change
Excessive wind change
Silo sample
Process sample
Wind speed <3 mph
Process sample
N.G. - no good
-------
Table 10. ERROR ANALYSIS
s
o
z
z '
H
0
m
m
> 00
o
i
o
o
Tl
O
H
O
Z
Run
Number
1
2
3
4
5
* Sf
Phosphate
Coal Storage Sand & Gravel Kaolin Processing . ,. Rock Processing
Emission Std. Con. Emission Std. Con. Emission
Rate Dev. Limit Rate Dev. Limit Rate
.1020 +.0689 +.1266 .8006 +.1215 +.223 17-92
.1777 ±.1384 +.2543 1.716 +.683 +.948 104.9
3.383
*
84.26
uncle omitted: See Table Q
Std. Con. Emission
Dev. Limit Rate
± .15 ± .28 169.3
±68.1 ±125.1 *
± 2.09 + 3.83 82.16
* * 250.2
+74.14 ±136.2 55.6 .
126.6
Std.
Dev. .
± 67-7
»
±40.8
±166.6
+ 5-35
+ 86.1
Con.
Limit
±124.4
*
±.75.0
±306.1
± 9.83
±158.2
Note: All values are in #/hr
-------
Table 11. EFFECTS OF WIND VELOCITY
Industry
2
o
z
(A
>
z
H
o
m
(/>
m
o
i
o
o
3
T)
O
71
>
H
Q
Z
Coal Storage
Phosphorus Rock
Processing
Kaolin-Processing
OJ
IV)
1Units in #/T
2Units in MPH
3Units in Deg.
Run
No.
1
2
1
4
6
2
5
Source
Storage
Rock Storage
Processing Area
Emission
Factor1
.0091
.0158
.434
.639
.322
13.98
11.23
Wind
.Speed2
3.8
6.1
6.6
14.0
6.3
6.1
4.1
Wind
Range3
52.4
40.2
51.6
30.7
42.5
46.9
53-4
-------
Wind
Azimuth
Meterological Station
Figure 1. Sampling arrangement
33
MONSANTO RESEARCH CORPORATION
-------
S
O
z
H
O
m
in
m
O
I
O
O
3J
"D
O
-j
O
Z
Time of Day
Noontime
Late AM, Early PM
Mid AM, Mid Pm
Early AM, Late PM
Insolation
Class
4
3
2
1
Wind
Speed
Calm
(0 - 2 mph)
Light
(2 - 5 mph)
Moderate
(5 -10 mph)
Strong
( >10mph)
Net Radiation Index
4
A
A
B
C
3
A
B
B
C
2
B
C
C
D
1
C
D
D
D
0
D
D
D
0
-1
F
E
D
D
-2
r
f
E
0
Stability C-ategories
Figure 2. Plow chart of atmospheric stability class determination
-------
Angle of Wind
B
Source
Resultant
Wind
Direction
D
Figure 3. Sampler positions
35
• MONSANTO RESEARCH CORPORATION
-------
S°UT
Dust
Analysis for
Suspect
Compound
Hi - Vol
Sample
(Filter)
Sample
Weight
Ambient Dust Load
*-Level of Fibrous Toxicity
Inorganic
Analysis
for Elements
Match and Identify
Toxic Material
Quantity of Toxic Substances
Figure^. Laboratory scheme
36
• MONSANTO RESEARCH CORPORATION •
-------
Filter
Spec's.
4—(Specified \
^ VlonditicW
Balance
Spec's.
|Sampler\
and
Operating;
Spec's.
\
Specified \
londitions/
Calculate
and
Document
It
Report
Results
\^=^^'
Figure 5- Functional analysis of high volume
. suspended particulate sampling.
37
• MONSANTO RESEARCH CORPORATION •
-------
2
O
z
z
-I
O
m
rn
o
i
o
o
73
-j
o
z
OJ
oo
Unloading
Ash Disposal
Storage of Coke
Storage
Figure 6. Coal processing
-------
2
O
z
Z
H
O
m
o
i
n
o
H
O
Z
U)
Clay Wastes
Coarse Aggregate
Storage
Pea Gravel Storage
Sand Storage
Figure 7. Sand and gravel processing
-------
Kaolin Storage Silos
Figure 8. Clay processing
• MONSANTO RESEARCH CORPORATION •
-------
2
0
z
3 Benificiated
jjj Phosphate Rock Ore
>
a i-1
o
I
o
o
3J
T)
0
^ P
Z
/V » • • /V
/•'"*•"'• ^^
/•? ? •»• >N —
^/V I * ? • •• \
-/** < *'* ' '*' • *'*y
/^-x^» •'"..' ~*'JisS
Drying
-^- and
Storage
Open Storage
Igure 9- Selected phosphate rock
m^mmmmt
s our
-^
OO (3O
Loading of Railroad Cars
ces
-------
SECTION VII
REFERENCES
1. Particulate Pollutant System Study, Handbook of Emission
Properties, Vol. VIII, Midwest Research Institute, PB-203522,
2. The Cost of Public Confidence in Nuclear Weekly Energy
Report, page 8, July 1, 1974.
3. TLV's® Threshold Limit Values for Chemical Substances in
Workroom Air Adopted by ACGIH for 1973, American Conference
of Governmental Industrial Hygienists.
4. Turner, B. D., Workbook of Atmospheric Dispersion Estimates,
U.S. Department of Health, Education and Welfare, Environ-
mental Science Services Administration, PHS 999-AP-26, 1970.
5. Minerals Yearbook, Vol I, Bureau of Mines, U.S. Department
Of the Interior, U.S. Government Printing Office, 1970.
MONSANTO RESEARCH CORPORATION
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