APPLICATIONS OF RECEPTOR MODELING METHODS
TO SOURCE APPORTIONMENT OF ARSENIC IN
THE RUSTON-TACOMA, WASHINGTON AIRSHED:
A FEASIBILITY STUDY
FINAL REPORT
Prepared For:
Puget Sound Air Pollution Control Agency
200 W. Mercer Street, Room 205
Seattle, Washington 98109
By:
NEA, INC.
10950 S.W. 5th Street, Suite 380
Beaverton, Oregon 97005
June 15,1984
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APLICATIONS OF RECEPTOR MODELING METHODS
TO SOURCE APPORTIONMENT OF ARSENIC IN
THE RUSTON-TACOMA, WASHINGTON AIRSHED:
A FEASIBILITY STUDY
Final Report
Prepared For:
Puget Sound Air Pollution Control Agency
200 W. Mercer Street, Room 205
Seattle, Washington 98109
By
John A. Cooper
James E. Houck
Lyle C. Pritchett
and
Clifton A. Frazier
NEA, INC.
10950 S.W. 5th Street, Suite 380
Beaverton, Oregon 97005
June 15, 1984
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ABSTRACT
The feasibility of using receptor modeling methods to apportion
sources of arsenic in the Tacoma-Ruston airshed near the ASARCO,
Incorporated copper smelter has been evaluated. Source resolvability
and quantification was evaluated by chemically characterizing
representative fine and coarse particle sources within the smelter
and settled dust samples outside the smelter. The elemental composition
of ten ambient particulate samples was also measured and arsenic levels
apportioned using chemical mass balance-methods.
It was concluded that receptor modeling using only chemical information
would probably not be able to adequately resolve and quantify the influence
of all key sources. It was also concluded, however, that one could
confidently expect to resolve all major sources responsible for high
arsenic levels by separating the ambient aerosol into fine and coarse
particles interpreting the data with both chemical mass balance and
multivariate analysis methods and relating these results to meteorologically
regime stratified arsenic data.
Upper limits for the contributions of several sources were established
as a result of the ambient filter analysis. It was also concluded from
the ambient filter analysis that coarse particle sources are probably
responsible for the majority of arsenic on high impact days studied.
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ACKNOWLEDGEMENTS
Completion of this study is the result of the support and
cooperation of a number of organizations and individuals. Support
for this study was provided by the Puget Sound Air Pollution Control
Agency (PSAPCA) through a grant from the Region X Environmental
Protection Agency. Washington State Department of Ecology (WSDOE)
provided staff assistance with source sampling and ASARCO, Inc.
provided both plant access and staff assistance with source sampling.
The source sampling assistance of Jim Nolan of PSAPCA and
Jay Wallenberg"of WSDOE is also gratefully acknowledged, as well as
the many helpful suggestions from Jim Nolan, who was also PSAPCA1 s
program director. The cooperation and assistance of the ASARCO
staff, both in Tacoma and Salt Lake City was particularly helpful and
is gratefully acknowledged.
ii
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TABLE OF CONTENTS
ABSTRACT i
ACKNOWLEDGEMENTS ii
LIST OF TABLES iv
LIST OF FIGURES v
1.0 INTRODUCTION 1
2.0 OBJECTIVES AND CRITERIA 2
3.0 EXPERIMENTAL 4
3.1 Source Sampling 4
3.2 Ambient Aerosol Samples 6
3.3 Elemental Analysis 6
4.0 RESULTS AND DISCUSSION 7
4.1 Fine to Coarse Particle Size Ratios 7
4.2 Elemental Analysis Results for Source Samples 8
4.3 Elemental Analysis Results for Ambient Samples 10
4.4 Chemical Mass Balance (CMB) Results 11
4.5 Source Resolution 12
4.6 Indirect Contribution of Historical Contamination 13
5.0 CONCLUSIONS 15
6.0 RECOMMENDATIONS 15
7.0 REFERENCES 18
iii
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LIST OF TABLES
Number Title Page
1 List of Potential Arsenic Sources and Estimated 21
Emission Rates (kg/hr)11
2 Summary of Sampling Data for Process Emissions 22
Sampled at the ASARCO - Tacoma Copper Smelter
3 Summary of Resuspension Data for Bulk Source Samples 23
Collected from the ASARCO - Tacoma Copper Smelter
4 Average Fine to Coarse Particle Ratios After 24
Correcting for Fine Particles Deposited on Coarse
Particle Filter
5 Percent Elemental Composition of Herreschoff Roaster 25
Charge and Calcine
6 Percent Elemental Composition of Particles in Road 26
and Railroad Track Dust
7 Percent Elemental Composition of Slag 27
8 Percent Elemental Composition of High Arsenic Bulk 28
Samples
9 Percent Elemental Composition of Settled Dust Collected 29
Within the Plant and the Ore Concentrate
10 Percent Elemental Composition of Emission from Number 1 30
Brick Flue: Fine Fraction ( < 2.5 urn)
11 Percent Elemental Composition of Emission from Number 4 31
Converter Secondary Hood: Fine Fraction ( < 2.5 vm)
12 Percent Elemental Composition of Emission from 32
Reverbatory Furnace Slag Skim: Fine Fraction
( < 2.5 ym)
13 Percent Elemental Composition of Emissions from 33
Number 1 Brick Flue: Coarse Fraction ( > 2.5
14 Percent Elemental Composition of Emissions from the 34
Number 4 Converter Secondary Hood: Coarse Fraction
( > 2.5 ym)a
15 Percent Elemental Composition of Emissions from 35
Reverbatory Furnace Slag Skim: Coarse Fraction
( > 2.5 ym)
16 Comparison of Elemental Composition of Slag 36
17 Elemental Concentration of Ambient Samples (yg/m3)* 37
18a Correlation Matrix (10 Ambient glass fiber filters) 38
18b Slope Matrix (10 Ambient glass fiber filters) 38
18c Intercept Matrix (10 Ambient glass fiber filters) 38
iv
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LIST OF TABLES (Continued)
Number Title
19 CMBDEQ Results for CMB //MB338 39
20 CMBDEQ Results for CMB //MB338 39
21 CMBDEQ Results for CMB //MB338 40
22 CMBDEQ Results for CMB //MB338 41
23 CMBDEQ Results for CMB //MB335 41
24 CMBDEQ Results for CMB #MB335 42
25 CMBDEQ Results for CMB //MB336 42
26 CMBDEQ Results for CMB *MB337 43
27 CMBDEQ Results for CMB *MB337 43
28 CMBDEQ Results for CMB //MB334 44
29 CMBDEQ Results for CMB //MB334 44
30 CMBDEQ Results for CMB #MB033 45
31 CMBDEQ Results for CMB #MB033 45
32 CMBDEQ Results for CMB #MB037 46
33 CMBDEQ Results for CMB //MB037 46
34 CMBDEQ Results for CMB #MB029 47
35 CMBDEQ Results for CMB //MB032 47
36. CMBDEQ Results for CMB #MB038 48
37 List of Source Code Definitions 49
38 Maximum Source Contributions 50
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LIST OF FIGURES
Number Title Page
1 Plot of the percent Al and Si for combustion 51
and geological sources. These sources would
be difficult to resolve using only these two
elements (dimensions).
2 Three dimensional plot of the Fe, Al, and Si in 52
geological type samples. The addition of the Fe
dimension effectively improved the source
resolving capability, i.e., the angle between the
coal fly ash and crustal average has increased.
3 Three dimensional plot for the As, Al, and Si 52
composition in geological samples. The addition
of As *has greatly improved the separation of the
fine coal fly ash from the other sources. Other
coal fly ash samples have been reported to contain
even higher As concentrations.
4 Physical layout of the ASARCO-Tacoma smelter 53
showing the location of the bulk samples
collected for analysis.
5 Vectorial representation of three elements 54
from selected source profiles.
6 Vectorial representation of three elements 55
from selected source profiles.
7 Schematic categorization of sources based 56
on chemistry and particle size
8 Illustration of direct and indirect smelter 57
impacts on air quality. (From Kellogg,
report, NEA).
9 Schematic diagram of the sources and sinks 57
of aerosolizable dust.
10 Percent quarterly lead levels at Silver King 58
School Kellogg, Idaho.
11 Percent quarterly lead levels at a doctor's 59
clinic in Kellogg, Idaho,
vi
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1.0 INTRODUCTION
ASARCO, Incorporated (ASARCO) currently operates a primary
copper smelter in the Tacoma-Ruston, Washington area. The smelter
began operation in 1890 as a lead smelter, and was later converted to
a copper smelter capable of processing high arsenic copper ores.
Operation of the smelter over the past century has contaminated the
local area with hazardous elements such as arsenic, cadmium and lead,
as well as other elements. Although emission rates of these elements
have been reduced substantially in recent years by the addition of
pollution control equipment, arsenic levels are still high and of
concern.
Section 112 (b) (1) (B) of the Clean Air Act (42 U.S.C. 7412)
requires establishment of national emission standards for hazardous
air pollutants (NESHAP) which will effectively maintain ambient air
quality. To do this, however, requires the development of control
strategies based on an accurate, quantitative knowledge of the major
sources within a plant responsible for high levels of hazardous
pollutants such as arsenic.
Numerous emission inventory and dispersion modeling studies of
the smelter have been conducted in recent years. (1-11) The results
from these studies, however, have not provided an adequate level of
understanding to develop and implement a control strategy with a
high level of confidence that it will be effective in improving air
quality. These classical dispersion modeling methods are severely
limited in this particular case because of the complex terrain,
unknown micrometeorology, and difficulty of modeling low level
fugitive emissions which exhibit large variations in daily absolute
emission rates, in addition to there being potentially large
contributions from frequent accidental releases.
Receptor modeling methods, (12-14) however, require only a
knowledge of the relative chemical and physical characteristics of
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emissions to quantitatively apportion source contributions to parti-
culate levels. This approach, in contrast to dispersion modeling,
does not require absolute emission rates or meteorological data. These
methods have been successfully applied to numerous other complex
airsheds, including four airsheds with lead smelters. Quantitative
source impacts are calculated with receptor methods based on the
relative chemical composition of an ambient aerosol at a receptor
and that of potential source emissions. A major limitation of this
method is that it cannot resolve the influence of sources having
similar chemical composition unless Other features, such as particle
size, time and spatial variability, etc. are included in the analysis.
This problem of potentially poor source resolution due to similar
chemistry (multicollinearity) is of particular concern with respect
to the Tacoma-Ruston smelter, because many of its sources of arsenic
are expected to have a similar chemical composition.
The objective of this study is to evaluate the feasibility of
using receptor modeling methods to identify and quantify the contri-
bution major arsenic sources within the ASARCO smelter make to
ambient arsenic concentrations.
The approach taken is to first define potential study objectives
to be met or hypotheses tested by a receptor model study, establish
evaluation criteria, and characterize potential major arsenic sources
to determine if they are potentially resolvable.
2.0 OBJECTIVES AND CRITERIA
The feasibility and success of any study depends on how complete
its objectives are met and hypotheses answered. Although It is clear
that the objective of any receptor modeling study would be to identify
and quantify major arsenic sources within the smelter, it is
just as clear that this objective can only be met to a degree within
practical limits of resources. It is thus essential to establish
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source impact hypotheses based on previous studies, and then ask
vhich of these specific sources can be resolved and quantified.
Potential arsenic sources have been divided by the EPA (11)
into process and fugitive emissions as indicated in Table 1. Indirect
resuspension has been added to account for the potential contribution
contaminated road and soil dust make to ambient levels. The question
now is whether or not the converters fugitive emissions can be
resolved from the process ducted emissions, slag and matte tapping,
miscellaneous or arsenic building emissions.
The criteria for receptor modeling feasibility are thus based on (1) the
ability of a receptor modeling study to resolve the impacts of the potential
major sources, and (2) quantify the impacts of the major sources.
Source resolution is discussed in detail in reference 16. It
refers primarily to the degree of difference in the characteristic
features associated with each source. These features can include
chemical composition
particle size
point of emission (height, geographical), and
time variability patterns.
Source resolution from the chemical point-of-view, refers to
the angle between two source vectors when plotted in elemental space.
Examples of this are illustrated in Figures 1-3. Figure 1 is a plot
of the Al and Si concentration of soil, road dust, coal, fly ash,
average earths crust, asphalt production, and emissions from a rock
crusher. The coordinates of each data point represents the end
point of a vector from the origin to the data point. The influence
of these sources could not be easily resolved on the basis of their
Al and Si chemistry alone. That ±s the angle between the vectors
leading to each data point is small relative to the uncertainties.
The angle between source vectors can be increased, however, by
increasing the dimensionality of the space as illustrated in Figure 2,
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which shows the addition of Fe. In this case, the solid angle between
the sources has been increased, but only slightly. The addition of
an As dimension greatly improves the resolvability of fine particle
coal fly ash from the other crustal sources. Further source resolvability
could be obtained by collecting only fine particles, since 90 to 95% of
soil derived material is greater than 2.5 ym, and road dust can be
further resolved from soil because of their characteristic traffic and
windspeed dependencies.
Although a source may be easily resolved, it may not be accurately
quantified because of a highly variable chemical composition. The mass
attributed to s particular source is directly proportional to the
chemical composition of the fitting elements used in the source profile.
Large uncertainties in these source profiles yield large uncertainties in
source contributions, even though it may be readily resolvable from
other interferring sources.
3.0 EXPERIMENTAL
3.1 Source Sampling
Samples of emissions from selected potential sources were collected
and analyzed to determine which key sources could be resolved, based
on their chemical composition and particle size characteristics.
Source samples collected are listed in Tables 2 and 3. Although the
sources sampled do not include all potential sources, they represent
those sources thought to be major contributors and are representative
of the range of emissions expected.
NEA's size-segregating dilution sampler (SSDS) (17) was used to
collect fine and coarse particle samples of the emissions in the number 1
brick flue which are representative of the stack emissions. This
sampler extracts an isokinetic sample, dilutes and cools the emissions
to near ambient conditions, and separates the particles into fine
(< 2.5 urn and coarse (> 2.5 pm) particle fractions with a virtual
dichotomous impactor.
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The lower temperature emissions from slag tapping and the
secondary converter hood were sampled both with a virtual dichotomous
impactor and a low-volume TSP sampler. Although the converter emissions
were sampled in the secondary hood, they are expected to be representative
of fugitive emissions from converters in general. The slag tapping
emissions sampled are expected to be representative of emissions during
slag dumping.
Bulk samples of fifteen representative fugitive dust sources
were collected by PSAPCA staff. Road dust samples were collected with
NEA's paved road dust sampler, which collects the material on a glass
fiber filter. The locations where the bulk samples were collected are
indicated on tfie map shown in Figure 4.
Samples collected with NEA1 s SSDS, dichotomous sampler, and the
low-volume TSP sampler were returned to the laboratory where the filters
were weighed and analyzed nondestructively by X-ray fluorescence
without prior sample preparation.
Four high arsenic bulk samples were resuspended without further
preparation, while the other bulk samples were dried at 65°C overnight
and sieved prior to resuspension.
Material passing through a 400 mesh screen (38 um) was aerosolized
in NEA's resuspension chamber and sampled with a virtual impactor
dichotomous sampler. Fine ( < 2.5 vo) and coarse ( > 2.5 urn, < 15 urn)
particle samples were collected on teflon filters and weighed. The
aerosolization process was continued until an appropriate amount of
mass was deposited on each filter. Because of the predominance of
coarse particles in the bulk samples» the coarse particle filters
reached appropriate deposit levels much more rapidly than the fine
particle filter. The fine to coarse (F/C) particle ratio was determined
from the filter deposit masses measured when the appropriate mass
level was reached on the coarse particle filter. The coarse particle
filter was then replaced with a scrap teflon filter, and the aerosoli-
zation-sample collection process continued until a sufficient level of
material was collected on the fine filter for analysis.
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The aerosolization chamber and dichotomous sampler were both
completely dismantled and thoroughly cleaned between each sample to
minimize the possibility of contamination. Samples containing the
lowest arsenic and lead concentrations were aerosolized prior to
those with high arsenic concentration to further minimize the
possibility of contamination.
The slag dump fines and Lepanto copper ore concentrates were
aerosolized and sampled with a low-volume TSP sampler, because none
of the material passed through the 400 mesh (38 urn) screen.
3.2 Ambient Aerosol Samples
Ten ambient aerosol samples were selected by PSAPCA for analysis
to represent high arsenic impact days. The samples were collected
with high-volume TSP samplers on glass fiber filters at sampling
sites P2 (26th & Pearl), P14 (47th and Baltimore), and P15 (Rustin
Elementary School). The P2 site is located about two miles southeast
of the plant. The P14 site is two blocks east of the main stack, and
the P15 site is across the street from the plant parking lot, south of
the smelter. Disks 47 mm in diameter were cut from the filters for
X-ray fluorescence analysis.
3. 3 Elemental Analysis
The elemental composition of source and ambient aerosol
samples was determined using energy-dispersive X-ray fluorescence
analysis. Standard thin-film methods (18, 19) were used to quantify
the elemental composition of the deposits.
Special analysis conditions, however, were required because of
the unusual elemental ratios. Analyte lines (K-X-rays) for Ag, Cd,
In and Sn have spectral interferences from the As and Pb sum peaks.
These interferences were eliminated by analyzing the samples using
post copper filters to absorb the As and Pb X-rays prior to analysis.
L - X-ray lines from As, Ag, Cd, In, Sn, and Sb also interferred
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with the analysis of light elements, such as Mg, Al, K, and Ca,
which substantially increased the minimum detection limits for these
elements.
The glass-fiber filter analysis was limited by its high elemental
blank content, and, as a result, only a few elements could be reliably
quantified on these filters.
Some elements, such as Tl and Bi, were observed, but only semi-
quantitative results were reported because validated standards were
not available.
4.0 RESULTS AND DISCUSSION
4.1 Fine to Coarse Particle Size Ratios
The fine to coarse particle size ratios were determined using
virtual dichotoraous impactors with fine to coarse size cuts as follows:
Process Samples
Fine: < 2.5 pm
Coarse: > 2.5 pm
Bulk Aerosolized Samples
Fine: < 2.5 urn
Coarse: > 2.5 urn but < 15 urn
The main difference between the process samples and the bulk samples
was in the upper cut point for the coarse particle fraction. An
upper cut point for the coarse particles in the process emissions was
not established, while the standard 15 urn inlet was used to sample
the aerosolized bulk samples. This will have essentially no impact
on the characteristics of material collected with similar samplers
with 15 pm cut points in the ambient environment because the fraction
of coarse particles in the process emissions is so small.
The F/C particle ratios for the aerosolizable bulk samples were
based on intermediate mass determinations made after enough material
had been collected on the coarse particle filter. The fine particle mass
listed is the mass obtained after additional aerosolization steps
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using a scrap coarse particle filter. The listed fine particle mass
was not used in the F/C particle ratio calculations.
The average F/C particle ratios are listed in Table 4. The sources
are easily grouped into fine particle process emission sources in
which about 95% of the particulate mass is less than 2.5 urn, and coarse
particle bulk samples representing fugitive dust sources in which
coarse particles represent more than 90% of the mass.
The fine to coarse particle ratio for the "Slag Dump Fine" sample
and the Lepanto Copper Ore Concentrate could not be determined because
insufficient material passed through the 400 mesh sieve ( < 38 ym).
This clear distinction between fine and coarse particles will be
particularly valuable in resolving the influence of possible sources.
4.2 Elemental Analysis Results for Source Samples
Elemental analysis results are presented for the source samples in
Tables 5-15. The elemental composition obtained in this study for the
coarse particle fraction of the composite slag sample (Table 7) is compared
in Table 16 with an earlier bulk analysis of slag using semiquantitative
spectrographic analysis and atomic absorption methods. (7) Good
agreement is obtained for most elements, particularly when one considers
that the analysis results are based on completely different slag
samples. The main exceptions are iron, which differs by a factor of two,
and copper and lead which differ by 20 to 4 fold. Still lower iron
concentrations were measured in the fine particle fraction of the slag
samples analyzed in this study (Table 7). This difference in a major
species, such as iron, is thought to be due to differences in samples
and not an analytical artifact associated with this analysis.
Errors greater than indicated in the tables can exist for the
coarse filters collected from the aerosolized bulk samples because of
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the loss of particles prior to analysis. The coarse particles were
poorly held to the teflon filters, and great care in handling was
required to minimize particle loss after weighing. Even so, some coarse
particles were lost from the filters as determined by weighing the
filter after XRF analysis. In cases where particle loss was indicated,
the weights after XRF analysis were used to calculate the percent
composition. This potential problem, however, is not expected to
affect the elemental ratios which are used in resolving the influence
of specific sources. It would affect the quantification, but could be
minimized in any future study by using oil coated filters which have been
demonstrated to minimize the loss of particles even after being dropped
in a shipping Container. (20)
The sampling and analysis replication is best illustrated by the
analysis results for the process samples. (Tables 10-15) The
variability in the values obtained for the fine fraction samples
representing slag dumping emissions and the stack emissions was
in the 10 to 15% range over four samples collected over a period
of sixteen hours for the slag samples, and 37 hours for the stack
samples. The mass determinations for the process samples were quite
stable and easily replicated. Thus, the uncertainties in the absolute
percent compositions are expected to be accurately represented by the
indicated uncertainties.
The uncertainties in the coarse particle composition of the process
emissions is quite high because there was limited amount of mass
collected on these filters to begin with, and the coarse particle
composition was calculated by subtracting the fine particle mass that
had deposited on the coarse particle filters. The resulting large
uncertainties in the coarse particle fraction of the process emissions
will not have a substantial impact on the feasibility of any receptor
modeling study, because of the small contribution they are expected to
make to ambient levels.
The bulk samples can be divided into two categories: samples
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with high Al and Si, and those with low Al and Si concentrations.
The four high As samples (S02 Cottrell dust, No. 1 flue dust, As
baghouse pad dust and the As plant product), and the Lepanto copper
ore concentrate fall into the latter category of low Al and Si
content. Within this category, the ore concentrate is easily separated
from the other fine sources based on its high Fe and Cu concentration
relative to As, Pb and Sb. The S02 Cottrell dust is also characteris-
tically different because of its high Pb concentration relative to Sb,
As, Cu, An, and Fe. The No. 1 flue dust falls in between the Cottrell
dust and the two remaining samples (As baghouse pad dust, and the As
plant product) which are quite similar in composition.
The three process emissions fall into two rsily distinguishable
categories: one consisting of the slag dumping (skimming) emissions
having high As concentrations relative to Pb, and the other category
consisting of the converter and stack emissions having Pb levels
comparable to the As concentrations.
These general categories are simply established on the basis of
easily recognizable differences. Further categorization may be
possible by taking into account more subtle differences in chemistry.
4.3 Elemental Analysis Results for Ambient Samples
The elemental compositions of ten high arsenic ambient aerosol
samples are listed in Table 17. The samples were collected on glass
fiber filters with high-volume TSP samplers. The analysis of, these
samples was limited because of the high elemental background concen-
trations in the glass fiber filters, and their high degree of
variability. The concentration of Fe, Cu, As, and Pb are about 1%,
while the concentration of Sb runs about 0.1%. These concentrations
are about 10 fold above the blank filter concentrations, and are not
expected to be substantially affected by variations in blank
concentrations.
10
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Table 18 summarizes the results of bivariate plots of these five
elements. From the correlation matrix in Table 18a it appears that Cu,
As, Sb, and Pb are highly correlated, even after accounting for the
correlation affect due to common variability caused by meteorology.
Although it is difficult to draw strong conclusions from this very
limited data set, it suggests that the variability of copper and lead,
for example, is dominated by a single source or a group of interdependent
sources with an average copper to lead ratio of about 1.55. There are
a number of sources with Cu to Pb ratios close to this value, such as
road dust, railroad track dust, slagi As baghouse pad dust, and the
final product. On the other hand, a number of other sources, such as
process emissions, could be eliminated as substantial contributors to
these species because of their very low Cu to Pb ratios, unless these
sources were highly dependent on another source with a very high Cu
to Pb ratio. Other conclusions might be reached by examing other
correlations and elemental ratios. With larger data sets, source
profiles, and particle size information, potential sources can often
be quickly eliminated or identified as a possible contributor with
a high degree of confidence using multivariate analysis techniques such
as factor analysis.
4.4 Chemical Mass Balance (CMB) Results
The five elements, Fe, Cu, As, Sb, and Pb, with the highest degree
of confidence from the analysis of the ambient filters were selected
for inclusion in our CMB analysis. The CMB results from selected
calculations are listed in Tables 19-36. The source codes are defined
in Table 37. It is clear from these results that a number of possible
source combinations could explain the limited ambient data.
Even though the most probable source contributions cannot be
unambiguously defined on the basis of CMB analysis of the limited
ambient data, definitive results concerning the maximum impact from
some sources and the likely characteristics of other sources can be
derived from even this limited ambient data set.
11
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Table 38 lists the maximum possible impacts from a selected set
of sources for 4 of the days with highest As levels. These maximum
impacts were calculated by assuming all of the Fe or Cu was contributed
by the source listed in the table. Since other sources are certain
to have contributed to these relatively common elements, the actual
contribution each source makes to As levels is expected to be much less.
In some cases, this maximum level is not too restrictive as
indicated by the railroad track dust near the south gate and other high
As sources not listed. On the other hand, it is clear that slag could
not have contributed more than about 10% of the As and probably a lot
less since much of the Fe is usually derived from road and other
windblown dust.
The high Cu to As ratio in the ambient particles suggests the need
for a substantial contribution from sources with high Cu concentrations.
Since fine particle process emissions from the stack, converter, and
slag pouring are deficient in Cu, coarse particle sources such as
roaster calcines, etc., must have made substantial contributions to
Cu levels, as well as As levels. Although high Cu sources cannot
explain all or even most of the As, their contribution, which is
required to explain Cu levels, suggests mechanisms which might cause
other high As coarse particle sources to make substantial contributions
to ambient As concentrations.
Additional source contribution restrictions, as well as more
precisely defined CMB source contributions could be developed with
fine and coarse particle sampling and measurement of more elements
in the ambient aerosol.
4.5 Source Resolution
Measurement of additional elements in the ambient aerosol would
greatly improve the method's ability to resolve the influence of
specific source impacts. The affect of adding different elements is
12
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illustrated with the two plots shown in Figures 5 and 6. Figure 5
shows that three source groups can be resolved if their elemental
concentrations are plotted in the As-Pb-Cu coordinate system shown.
In this sytem, however, slag particles cannot be resolved from other
sources such as the Martin Mill and Herreshoff roaster. Figure 6,
however, shows the same sources but plotted in a coordinate system in
which the Pb has been replaced by Fe. In this coordinate system,
there are nearly three orthogonal source groups. The slag which was
previously unresolved is now completely resolved from the other sources.
Figure 7 shows a schematic flow diagram in which sources are
successively resolved based on the addition of elemental content
information, particle size, or emission characteristics. Although not
all of these sources would be resolved as simply as indicated, from a
real ambient sample, it is likely that the influence of key contributing
sources could be resolved with appropriate experimental design.
In addition, other chemical and physical features can be measured
and used to resolve source influences. Of particular interest is the
chemical form of As and S. Distinction between sulfide and sulfate
and arsenic trioxide and pentoxide might be useful. This feasibility
study did evaluate the utility of making compound distinctions, but a
definitive conclusion on the utility of this type of data was not
reached. Wet chemical, ion chromatographic and X-ray diffraction
methods were considered (21-33), but not evaluated in the laboratory
because (1) the species are not stable in the environment and their
quantitative utility would have to be established prior to their use,
(2) the cost of the analyses appeared to be high, and (3) preliminary
indications suggested that additional speciation might not be necessary.
In addition, private communications (34) and review of recent literature
was not encouraging (21-38).
4.6 Indirect Contribution of Historical Contamination
This smelter has been in operation in the Ruston area for nearly
a century. During this time its emissions have contaminated the local
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area with As and other hazardous elements. The indirect contribution
of historical contamination has been thought to be a significant
potential contributor of current ambient As levels.
Indirect area sources of As and other pollutants are defined as
sources of aerosolizable dust which has been previously contaminated
by the smelter. Figure 8 illustrates a comparative example of direct
and indirect sources of pollutants, both of which originate from the
smelter. Although there are many different types of surfaces which
can act as indirect sources, there are only two significant resuspension
forces, traffic and wind. Even though both of these resuspension
forces yield emissions with similar chemical composition, their
impacts can be easily resolved on the basis of significantly different
time dependence.
The contaminated surfaces can have significantly different physical
and chemical characteristics. A physical model describing the
aerosolizable dust layer (39) on contaminated surfaces is illustrated
in Figure 9. The aerosolizable dust layer by definition must be
quite mobil and have a relatively short lifetime. The transition
zone, on the other hand, could vary from zero on paved roads, roofs,
etc. to a foot thick in tilled gardens. The bulk chemistry of the
aerosolizable dust layer on unpaved dirt (soil) surfaces will take
on the bulk soil composition plus the fallout impurities. The
aerosolizable dust layer on leaves and roofs will consist primarily
of the average fallout composition. The aerosolizable dust layer on
paved roads will consist of a mixture of material from transportation
sources (oil, brakes, exhaust, etc.). tracked-on soil, road wear and
fallout. Paved road dust, however, is usually very similar in bulk
chemical composition to the surrounding soils plus fallout impurities.
Of critical importance is the lifetime of pollutants in this
aerosolizable dust layer. The lifetime of pollutants on paved
surfaces must be short because of the forces acting on their removal
14
-------
and the absence of a transition zone. This has been confirmed by
measuring the rate ambient particulate lead concentrations decreased
in Kellogg, Idaho, after closure of the Bunker Hill lead smelter in
1981. Figures 10 and 11 show that the time required for the ambient
particulate levels to decrease to half their average value during
the plant operation is about 4 months. It should also be noted that
ambient levels are now averaging about 0.2 yg/m3 which is the level
calculated by CMB methods prior to plant closure for automotive
tailpipe contributions. Thus, As in the aerosolizable dust layer on
paved surfaces is likely to have originated from very recent fallout,
mainly from the smelter, and track-out or erosion from contaminated soil,
5.0 CONCLUSIONS
Receptor modeling based solely on particulate chemistry cannot
resolve and quantify the contribution each potential source makes
to ambient arsenic levels.
Receptor modeling combined with fine and coarse particle sampling,
multivariate analysis, and meteorological regime stratification of
ambient levels can confidently be expected to resolve the
influence of major sources and accurately quantify their contributions,
Historical contamination of the area is not a significant cause of
high ambient levels of As.
Resuspension of recently contaminated road dust and other area
dusts may be significant (about 5 to 10%) contributors to high As
levels.
Coarse particle sources are thought .to be responsible for the majority
of the ambient As, if the high As days studied are typical of high As
days throughout the year.
6.0 RECOMMENDATION S
A receptor modeling study of the Tacoma-Ruston ASARCO copper
smelter is recommended on the basis of this feasibility study. This
recommended study should include the following components to effectively
15
-------
resolve and quantify the contribution major sources make to high As
impact days:
1. Historical As and meteorological data should be used to
develop a meteorological regime stratification of As
values. This will provide the data required to relate
future results to typical meteorological conditions.
2. Daily sampling should be conducted at two sites (P14 and P15)
with dichotomous samplers. All filters should be measured
for As and other easily measured major species.
3. This ambient data set should be analyzed by multivariate
analysis methods.
4. Particulate samples collected on high As days should be
analyzed in more detail for both major and trace species.
5. The data set for high As days should be analyzed by multi-
variate methods.
6. Additional source sampling is recommended so as to
characterize other potential major sources not measured
in this feasibility study and to define the variability of
source emissions.
7. The multivariate and source sampling results should be
combined to determine (a) the primary sources responsible
for As variability and (b) develop the most realistic,
validated source profiles.
8. Chemical Mass Balance (CMS) methods should be used to
quantify the contribution each source makes to fine and
coarse particle As levels.
9. The CMS and multivariate analysis results should be compared
with general expectations based on meteorology and records
of events within the plant.
10. Aerosolizable dust outside the plant boundaries shodld be
sampled, analyzed, and source contributions to As in this
dust determined to assess primary sources responsible for
indirect contributions.
The above study components are recommended for a basic program
which should adequately resolve and quantify source contributions.
If this is not adequate for some sources, short term sampling (2 to 4
hours) during special periods or episodes, in addition to the inclusion
16
-------
of more detailed chemical analyses may be required. Emission
inventory scaling might also provide useful insight into source
contributions.
17
-------
7.0 REFERENCES
1. Telecon. Whaley, G., Pacific Environmental Services, with White, T.,
ASARCO, Inc. April 8, 1983. Arsenic material balance for
ASARCO-Tacoma.
2. TRW Environmental Engineering Division. Emission Testing of ASARCO
Copper Smelter, Tacoma, Washington. EMB Report No. 78-CUS-12.
April 1979.
3. TRW Environmental Engineering Division. Emissions Testing of ASARCO
Copper Smelter, Tacoma, Washington. EPA Contract No. 68-02-2812,
Work Assignment Ho. 45. August 22, 1979.
4. "Survey of Potential Sources of Fugitive Arsenic Emissions at the
ASARCO, Tacoma Smelter". Author and date unknown, provided by
J. Nolan, PSAfCA, 1984.
5. "Potential Arsenic Emissions From Road and Field Dust Around ASARCO,
Tacoma Smelter". Author and date unknown, provided by J. Nolan,
PSAPCA, 1984.
6. Cowherd, C. and P. Englehart, "Emissions of Contaminated Soil Around
the ASARCO Tacoma Smelter", Midwest Research Institute draft report,
October 3, 1983.
7. "Final Report of Source Tests for Particulate and Arsenic Emissions
from Reverbatory Furnace Stag Skimming: ASARCO-Tacoma, Copper Smelter",
PSAPCA, Seattle, V3A., November 19, 1982,
8. Crecelius, E. , private communication, April, 1984.
9. "Determination of the Possibility of Arsenic Volatilization from
Tacoma Reverbatory Slag During Slag Handling", ASARCO report
No. 5053, December 27, 1982.
10. "ASARCO Air Curtain Test Project Preliminary Draft Report" from
C. Bruffey of Pedco to J. Nolan of PSAPCA, March 22, 1983.
11. "Inorganic Arsenic Emissions from High Arsenic Primary Copper
Smelters - Background Information for Proposed Standards", U.S.
EPA report No. EPA-450/3-83-009a, April 1983.
12. Friedlander, S.K., Env. Sc. Tech., 7, p. 235.
13. Cooper, J.A. and J. G. Watson, Jr., JAPCA, 1980, 30, p. 1116.
14. Gordon, G.E., Env. Sci & Tech., 1980, 14, p. 792.
15. Lead smelter studies in Kellogg, ID., East Helena, MT., Seattle, WA.,
and East St. Louis, IL. by NEA, Inc., Beaverton, OR.
18
-------
16. Cooper, J.A., "Receptor Approach to Quantitative Source Apportion-
ment of Chemical Pollutants in the Environment", NEA course notes
for Ontario Ministry of the Environment, November 14, 1983.
17. Houck, J.E., "Dilution Sampling for Chemical Receptor Source
Fingerprinting", Proc. 75th Meeting APCA, New Orleans, June 1982.
18. Rhodes, J.R., A. Pradzynski, R.D. Sieberg, T. Furuta, "Application
for a Si (Li) Spectrometer to X-Ray Emission Analysis of Thin
Specimens", Application of Low Energy X- and Gamma Rays,
(C.A. Ziegler ed, pp. 317-334, Gordon & Breach, Publ., 1971.
19. Nielson, K.K., "Matrix Corrections for Energy Dispersive X-Ray
Fluorescence Analysis of Environmental Samples With Coherent/Incoherent
Scattered X-Rays", Anal. Chem. 48(4), pp. 645-648.
20. Dzubay, T. , u\S. EPA, Research Triangle Park, NC, private communica-
tion, February 1984.
21. Davies, B.E., ed., Applied Soil Trace Elements, John Wiley & Sons,
N.Y., 1980.
22. Braman, R.S., C.C. Foreback, "Methylated Forms of Arsenic in the
Environment", Science, 182, pp. 1247-1249, 1973.
23. Kuroda, R. , S. Tatsuya, Y. Misu, "Anion-Exchange Behavior and
Separation of Metal Ions on DEAE-Cellulose in Oxalic Acid Media".
Talanta. 26, pp. 211-214, 1979.
24. Crecelius, E.A., M.H. Bothner, R. Carpenter, "Geochemistries of
Arsenic, Antimony, Mercury, and Related Elements in Sediments of
Puget Sound", Environmental Sci. & Tech., 9, pp. 325-333, 1975.
25. Andreae, M.O., "Determination of Arsenic Species in Natural Waters",
Analytical Chemistry, 49, pp. 820-823, 1977.
26. Leslie, A.C.D., H. Smith, "Napolean Bonaparte's Exposure to Arsenic
During 1816", Archives of Toxicology, 41, pp. 163-167, 1978.
27. Jackson, M.L., Soil Chemical AnalysisAdvanced Course. A Manual of
Methods Useful for Instruction & Research in Soil Chemistry, Phys.
Chemistry of Soils, Soil Fertility and Soil Genesis. Revised from
original 1956 edition.
28. Cross, J.D., I.M. Dale, A.C.D. Leslie, H. Smith, "Industrial Exposure
to Arsenic", Journal of Radioanalytical Chemistry, 48, pp. 197-208,
1979.
29. Jacobs, L.W., J.K. Syers, D.R. Keeney, "Arsenic Sorption by Soils",
Soil Sci. Soc. Amer. Proc. , 34, pp. 750-754, 1970.
30. Saunders, W.M.H., "Phosphate Retention by New Zealand Soils and Its
Relationship to Free Sesquiozides, Organic Matter and Other Soil
Properties", Nev Zealand J. of Agricultural Res., 8, pp. 30-57, 1965.
19
-------
31. Sisler, H.H. , "Phosphorus, Arsenic, Antimony and Bismuth", pp. 106-152,
in M.C. Sneed & R.C. Brasted, eds. Comprehensive Inorganic Chemistry,
Vol. 5, New York: D. Van Nostrand Co., Inc., 1956.
32. Braman, R.S., D.L. Johnson, C.C. Foreback, J.M. Ammons, and J.L. Bricker,
"Separation and Determination of Nanogram Acmounts of Inorganic Arsenic
and Methyl-Arsenic Compounds", Analytical Chemistry, 49, pp. 621-625,
1977.
33. Crecelius, E.A. , "Modification of the Arsenic Specification Technique
Using Hydride Generation", Analytical Chemistry, 49, pp. 621-625,
1978.
34. Crecelius, E.A. , Battelle-Northwest, Richland, WA, private communication,
April 1984.
35. Arsenic, Nat. Academy of Sciences Monograph, Washington, D.C., 1977.
36. Eatough, D.J., N.L. Eatough, M.W. Hill, N.F. Mangelson, J. Ryder, and
L.D. Hansen, "The Chemical Composition of Smelter Flue Dusts",
Atmospheric Environment, 13, pp. 489-506, 1979.
37. Eatough, D.J., J.J. Christense, N.L. Eatough, M.W. Hill, T.D. Major,
N.F. Mangelson, M.E. Post, J.F. Ryder, and L.D. Hansen, "Sulfur
Chemistry in a Copper Smelter Plume", Atmospheric Environment, 13,
pp. 1001-1015, 1982.
38. Eatough, D.J., F.E. Richter, N.L. Eatough, L.D. Hansen, "Sulfur
.Chemistry in Smelter and Power Plant Plumes in the Western U.S.",
Atmospheric Environment, 15, pp. 2241-2253, 1981.
39. Cooper, J.A., "R.I. DeCesar, C.A. Frazier, J.E. Houck, and J.F. Mohan,
"Determination of Source Contributions to Air Particulate Lead and
Cadmium Levels in Kellog, Idaho Using the Receptor Model", Final
Report, NBA, Inc., Beaverton, OR, December 10, 1981.
20
-------
Table 1
List of Potential Arsenic Sources
and Estimated Emission Rates (kg/hr)
Process Ducted Emissions
Herreshoff Roasters (0.4)
Reverbatory Furnaces (9.5)
Converters (0.04)
Anode Furnace (0.02)
Arsenic Plant (7.3)
Fugitive Emissions
Roaster
Chargine
Leakage
Hot calcine discharge and transfer (0.03)
Smelting Furnace
Charging
Leakage
Matte Tapping (0.5)
Slag Tapping (0.03)
Converter slag return (0.01)
Converters (14)
Charging
Blowing
Skimming
Holding
Pouring slag and blister
Leakage
Anode Furnace (0.08)
Charging
Blowing
Holding
Pouring
Miscellaneous (0.3)
Dust handling and transfer
Ladles
Slag dumping
Stack cleaning
Flue pulling
Arsenic Building (0.6)
Indirect Resuspension
21
-------
Table 2
Summary of Sampling Data for Process Emissions Sampled
at the ASARCO - Tacoma Copper Smelter
Filter
ID
MF893
MC894
MLS 38
MF863
MC864
ML841
MF867
HC868
ML839
MF871
MC870
HL835
MF897
MC898
ML836
MF895
MC896
ML840
HF865
HC866
ML842
MF869
HC872
ML837
MF873
HC874
MF875
MC876
MF877
HC878
MF879
HC880
HF881
MC882
HF883
MC884
None
None
HH897
None
HH898
None
None
Run
No.
1
1
I
2
2
2
3
3
3
it
4
l>
1
1
I
2
2
2
3
3
3
It
t>
4
M
*1
*1
*l
*2
*2
*2
*2
*3
*3
*3
*3
*1
*1
*2
*2
*3
*3
3
Source Desciiption
Filter
Type
Slag skim, reverb, furnace T
Slag skim, reverb, furnace T
Slag skim, reverb, furnace T
Slag skim, reverb, furnace T
Slag skim, reverb, furnace T
Slag skim, reverb, furnace T
Slag skim, reverb, furnace T
Slag skim, reverb, furnace T
Slag skim, reverb, furnace T
Slag skim, reverb, furnace T
Slag skim, reverb, furnace T
Slag skla, reverb, furnace T
Convertor 04, secondary hood T
Converter tit, secondary hood T
Convertor 04, secondary hood T
Convertor tit, secondary hood T
Convertor #4, secondary hood T
Convertor 04, secondary hood T
Convertor #4, secondary hood T
Convertor #4, secondary hood T
Convertor lib, secondary hood T
Convertor 04, secondary hood T
Convertor 04, secondary hood T
Convertor 04, secondary hood T
01 Brick Flue
#1 Brick Flue
tfl Brick Flue
01 Brick Flue
01 Brick Flue
tl Brick Flue
01 Brick Flue
01 Brick Flue
III Brick Flue
01 Brick Flue
01 Brick Flue
01 Brick Flue
DSS Inlet, 11 Brick Flue
DSS Outlet, 11 Brick Flue
DSS Outlet, fl Brick Flue
DSS Inlet, 11 Brick Flue
DSS Outlet, 11 Brick Flue
DSS Inlet, 11 Brick Flue
Probe Impact Sample
T
T
T
T
T
T
T
T
T
T
T
T
8X10 glass
8X10 glass
8X10 glass
8X10 glass
8X10 glass
8X10 glass
Particle
Size (p)
< 2.5
> 2.5
TSP
< 2.5
> 2.5
TSP
< 2.5
> 2.5
TSP
< 2.5
> 2.5
TSP
< 2.5
> 2.5
TSP
< 2.5
> 2.5
TSP
< 2.5
> 2.5
TSP
< 2.5
> 2.5
TSP
< 2.5
> 2.5
< 2.5
> 2.5
< 2.5
> 2.5
< 2.5
> 2.5
< 2.5
> 2.5
< 2.5
> 2.5
Sample Time Vol
Start/Stop (m3
Sample
) Duration
Temperature "
Flue Ambient
C
D. Cham.
27-20:52/27-21:10 0.300 18 rain. -
27-20:52/27-21:10 0.300 18 mln. -
27-20:52/27-21:10 0.658 18 rain.
27-23:20/27-23:36 0.267 16 min.
27-23:20/27-23:36 0.267 16 min.
27-23:20/27-23:36 0.656 16 mtn.
28-00:04/28-00:28 0.401 24 min.
28-00:04/28-00:28 0.401 24 mln.
28-00:04/28-00:28 0.984 24 min.
28-00:50/28-01:06 0.267 16 mln.
28-00:50/28-01:06 0.267 16 min.
28-00:50/28-01:06 0.666 16 min.
27-20:48/27-20:58 0.167 10 min.
27-20:48/27-20:58 0.167 10 min.
27-20:48/27-20:58 0.455 10 mln.
27-21:01/27-21:38 0.618 37 min.
27-21:01/27-21:38 0.618 37 min.
27-21:01/27-21:38 1.629 37 rain.
27-23:13/28-00:18 1.086 65 min.
27-23:13/28-00:18 1.086 65 min.
27-23:13/28-00:18 2.054 65 min.
28-00:30/28-01:29 0.985 59 min.
28-00:30/28-01:29 0.985 59 mln.
28-00:30/28-01:29 1.711 59 mln.
27-19:46/28-09:38
27-19:46/28-09:38
27-19:46/28-09:38
27-19:46/28-09:38
28-10:42/28-19:31
28-10:42/28-19:31
28-10:42/28-19:31
28-10:42/28-19:31
28-19:57/29-08:51
28-19:57/29-08:51
28-19:57/29-08:51
28-19:57/29-08:51
27-19:46/28-9:38
27-19:46/28-9:38
28-10:42/28-19:31
28-10:42/28-19:31
28-19:57/29-8:51
28-19:57./29-8:51
3/27/84-3/29/84
13.83
13.83
13.83
13.83
8.82
8.82
8.82
8.82
12.90
12.90
12.90
12.90
13.83
13.83
8.82
8.82
12.90
12.90
hrs.
hrs.
hrs.
hrs .
hrs.
hrs.
hrs .
hrs.
hrs.
hr s .
hrs.
hrs .
hrs.
hr s .
hrs.
hrs.
hrs.
hrs.
85
85
85
85
82
82
82
82
85
85
85
85
85
85
82
82
85
85
10
10
10
10
11
11
11
11
7
7
7
7
10
10
11
11
7
7
16
16
16
16
16
16
16
16
13
13
13
13
16
16
16
16
13
13
Deposit
Mass (mg)
21.779
3.294
63.231
9.242
I .330
28.261
13.733
2.071
44.451
L0.044
1.490
33.441
0.093
0.024
38.427
3.774
0.487
58.342
8.468
1 .121
> 115
2.121
0.238
18.101
7.170
1.910
6.269
1.839
5.655
1.302
6.225
1 .460
6.744
1 .728
6.358
1.539
*Samples collected with NEA's size-segregating dilution m.impler.
T = teflon
-------
Table 3
Summary of Resuspension Data for Bulk Source Samples
Collected from the ASARCO - Tacoma Copper Smelter
Sample
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Sample Description
Slag dump composite
Slag dump fines
Martin Mill weighing floor
Roadway by fine ore bins
Roadway by Sample Bldg.
As Baghouse concrete pad
Road dust: 52nd & Bennett
Road dust: 49th & Baltimore
RR track, south gate
ffl flue dust
Herreshoff Roaster calcine
Lepanto Cu concentrate
S02 Cottrell dust
Herreshoff Roaster charge
As Plant product
Type
Sampler
Dichot
Dichot
Lo-vol
Dichot
Dichot
Dichot
Dichot
Dichot
Dichot
Dichot
Dichot
TSP
Dichot
Dichot
Dichot
Dichot
Dichot
Dichot
Dichot
Dichot
Lo-vol
Dichot
Dichot
Dichot
Dichot
Dichot
Dichot
Filter
ID
CB282
FB283
LB332
CB292
FB293
CB284
FB285
CB286
FB287
CB300
FB301
1B339
CB274
FB275
CB306
FB707
CB298
FB299
CB294
FB295
LB333
CB290
FB291
CB296
FB297
CB302
FB303
Net
Deposit (mg)
1.593
0.110
1.134
1.179
0.124
1.023
0.136
1.015
0.137
2.564
0.201
1.168
0.203
0.988
0.111
1.602
0.037
0.335
0.154
1.336
1.068
0.075
0.846
0.160
0.690
0.038
Fine to
Coarse Ratioa
0.040
0.040
NA
0.019
0.019
0.10
0.10
0.097
0.097
0.034
0.034
0.046
0.046
0.11*
0.11*
0.016
0.016
0.062
0.062
NA
0.033
0.033
0.050
0.050
0.055
0.055
Comments
< 7.5 u, > 38 y
deposit uneven
not sieved
not sieved
not enought material to resusp.
not sieved
not sieved
deposit splotchy
deposit splotchy
<75, >38y
not sieved
not sieved
deposit uneven
deposit uneven
not sieved
not sieved
N)
U>
aFine < 2.5 ym; Coarse > 2.5 pro, 15 pm. Ratio based on intermediate loadings. Additional mass added to fine filtei
*Large uncertainty in this ratio because mass on coarse filter was not reproducible
-------
Table 4
Average Fine to Coarse Particle Ratios After
Correcting for Fine Particles Deposited on Coarse Particle Filter
Source F/C Ratio
Reverbatory Furnace Slag Skimming 27
Converter Secondary Hood (254 ± 437)
No. 1 Brick Flue Gas Stream (7.2)
Herreshoff Roaster Charge 0.050
Herreshoff Roaster Calcine 0.062
A
Road Dust 48th and Baltimore 0.046
Railroad Track Dust (0.11)**
Slag Dump Composite 0.04
Slag Dump Fine « 0.01
S02 Cottrell Dust 0.033
No. 1 Flue Dust 0.016
As Baghouse Dust 0.034
As Plant Product 0.055
Roadway Dust by Fine Ore Bin 0.10
Roadway Dust by Sample Bldg. 0.097
Martin Mill Weighing Floor 0.019
Lepanto Copper Ore Concentrate « 0.01
** Large uncertainty in this ratio because mass on coarse filter
was not reproducible. This ratio is thought to be an upper
limit.
24
-------
Table 5
Percent Elemental Composition of
Herreshoff Roaster Charge and Calcine
Element
Al
Si
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Y
Mo
Ag
Cd
In
Sn
Sb
Te
Hg
Pb
Bi
Mass (yg!
F/C
Roaster
Coarse
CB296
2.1 ± .4
7.2 ± .8
10.4 ± 1.2
< 0.1
0.39 ± .09
1.13 ± .08
0.16 ± .03
< 0.03
0.036 ± .012
0.053 ± .015
10.5 ± .7
0.11 ± .01
18.5 ± 2.0
0.77 ± .09
6.5 ± .4
0.04 ± .02
< 0.2
< 0.01
< 0.03
< 0.02
0.10 ± .02
0.10 ± .02
0.045 ± .015
< 0.03
< 0.05
0.25 ± .05
< 0.05
< 0.04
1.90 ± .12
(0.3 ± .1)
846
0.050
Fine
FB297
4.3 ± .5
12.0 ± 1.0
12.4 ± 1.1
< 0.1
0.68 ± .07
0.78 ± .08
0.19 ± .02
< 0.03
0.05 i .01
0.076 ± .010
8.9 ± 0.7
0.15 ± .03
18.9 ± 1.5
0.85 ± .10
8.8 ± 0.7
0.04 i .02
< 0.2
< 0.01
0.03 ± .01
< 0.02
< .05
0.16 ± .05
< 0.1
< 0.1
< 0.1
0.2 ± 0.1
< 0.1
< 0.04
2.3 ± 0.2
(0.3 t .1)
160
0.050
Calcine
Coarse
CM294
2.4 ± .2
4.8 ± .5
8.1 ± .3
< 0.1
0.42 ± .08
1.62 ± .11
0.16 ± .04
< 0.04
0.04 ± .02
0.081 ± .018
13.0 ± .6
< .08
18.4 ± 1.9
1.02 ± .08
3.7 ± .6
< 0.03
< 0.2
< 0.05
< 0.04
< 0.04
0.12 ± .04
0.042 ± .010
< 0.02
< 0.02
< 0.02
0.076 ± .021
< O.i
< 0.04
1.75 i .10
(0.06 ± .03)
335
0.062
Fine
FB295
6.3 ± .6
13.5 ± 1.2
9.8 ± 0.9
< 0.1
1.01 ± .10
2.0 ± .17
0.26 ± .05
< 0.05
0.066 ± .015
0.137 ± .015
18.6 ± 1.5
0.11 ± .05
18.6 ± 1.5
1.09 ± .12
4.2 ± .4
< .05
< .3
< 0.04
.05 ± .02
< 0.03
0.16 ± .05
< 0.2
< 0.2
< 0.2
< 0.2
0.34 ± .16
< 0.2
< 0.1
2.13 ± .18
(0.3 ± .1)
154
0.062
25
-------
Table 6
Percent Elemental Composition of Particles
in Road and Railroad Track Dust
Element
Al
Si
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Hi
Cu
Zn
As
Se
Br
Rb
Sr
Y
Mo
Ag
ca
In
Sn
Sb
Te
Hg
Pb
Bi
Mass (Pg)
F/C
49E Road Dust
Coarse
CB274
5.7 ± .3
23.6 ±1.5
0.6 ± .1
< 0.04
0.62 1 .05
1.40 ± .0?
0.39 * .03
0.020 i .007
0.032 l .006
0.068 ± .007
6.3 1 .3
0.022 ± .005
1.13 1 .06
0.192 ± .012
2.A7 ± .15
< 0.02
< 0.1
< 0.01
0.022 i .005
< 0.005
0.025 ± .007
< 0.02
< .04
.03
.03
0.07 .02
0.02
0.023 .008
0.46 .03
(< .05)
1168
0.046
Fine
FB275
10.7 ± 0.8
35 0.4
1.2 0.2
0.2 0.1
0.87 0.07
1.53 0.13
0.45 0.05
0.027 .009
0.040 .006
0.094 .010
7.5 .6
0.034 .008
1.29 0.10
0.22 0.020
1.80 0.15
* 0.02
< 0.15
< 0.01
0.020 ± .008
< 0.010
< 0.030
< 0.04
< 0.1
< 0. 1
< 0.1
< 0.1
< 0. 1
< 0.05
1.14 ± .09
(< 0.1)
203
0.046
52B R.Dust
Total
MB339*
(0.39)
(0.17)
s 2.0
(0.04)
(0.09)
-
-
Railroad Track South Gate
Coarse
CB306a
0.2 ±0.1
1.5 ± 0.3
0.13 ± 0.08
< 0.05
0.05 s 0-02
0.11 * .04
0. 03 ± . 02
< 0.01
< 0.01
0.024 i .004
2.15 1 .15
0.010 ± .002
1.10 ± .06
0. 30 ± . 02
2.02 1 .12
< 0.02
< 0.15
< 0.01
< 0.01
< 0.01
< 0.02
< 0.03
< 0.1
< 0.05
< 0.05
0.084 ± .028
< 0.05
< .03
1.02 ± .05
(0.16 s .05)
988 »*
0.11**
Fine,
FB307b
2 ±0.5
10.5 J 1.3
1.5 « 0.5
< 0.1
0.3 t .1
0.7 i .2
0.12 ± .05
0.05 l .02
0.05 1 0.01
0.12 1 .02
11.6 ± 1.4
< 0.05
7.0 ±0.8
2.0 * 0.2
13-2 i 1.5
< 0.04
< 0.6
< 0.03
< 0.04
< 0.04
< 0.02
< 0.08
< 0.4
< 0.2
0.3 ±0.1
0.3 i 0.1
< 0.2
< 0.1
6.5 ± 0.7
(1.2 ± 0.3)
111
0.11**
Coarse
CB306C
1.3
9.8
0.65
< 0.33
0.33
0.72
0.20
< 0.065
< 0.065
0.16
14.0
0.065
7.2
2.0
5 13.2
< 0.13
< 0.98
< 0.065
< 0.065
< 0.065
< 0.13
< 0.20
< 0.65
< 0.33
< 0.33
0.55
< 0.33
< 0.20
6.7
1.0
°T1 * 0.042
bTl = 0.20Z
formalized Sb H 13.2
Insufficient material was collected for resuspension and the glass fiber filter vas cut in
half in the field so a deposit mass could not be determined. All numbers have been
normalized to an arsenic value of 2.0.
**Large uncertainty in this ratio because mass on coarse filter vas not reproducible and
particles would not stick to filter.
26
-------
Table 7
Percent Elemental Composition of Slag
Element
Al
Si
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Y
Mo
Ag
Cd
In
Sn
Sb
Te
Hg
Pb
Bi
Mass (pg)
F/C
Slag Dump Composite
Coarse
CB282b
1.8 ± 0.2
14.1 ± 0.9
0.58 ± .09
0.51 ± .04
0.42 ± .03
3.6 ± .2
0.30 ± .05
0.04 ± .01
0.11 ± .02
0.13 i .02
21.6 ± 1.4
0.041 ± .005
1.96 ± 0.10
1.74 ± 0.09
1.88 ± .12
< 0.01
< 0.1
<: 0.01
0.019 ± .004
< 0.01
0.100 ± .015
< 0.02
0.05 ± .01
< 0.04
< 0.06
0.19 ± 0.03
< 0.04
< 0.03
0.89 ± .08
(= .05)
1593
O.OA
Fine
FB283
3.1 ± 0.6
15.8 ± 1.6
1.5 ±0.3
0.82 ± 0.12
0.40 ± .06
2.3 ± .3
0.21 ± 0.05
0.04 ± .01
0.08 ± .02
0.10 1 .02
14.1 ±1.5
0.043 ± 0.015
2.5 ± 0.3
1.62 ± 0.17
2.5 ± 0.3
< 0.01
< 0.1
< 0.01
< 0.03
< 0.03
< 0.05
< 0.08
< 0.3
< 0.3
< 0.3
< 0.3
< 0.3
< 0.2
1.28 ± .14
(< 0.2)
110
0.04
Slag Dump Fine
Total
LB3323
3.8 ± 0.2
21.7 ±1.2
0.69 ± 0.05
0.17 ± 0.02
0.79 ± 0.05
5.3 ± 0.3
0.24 ± 0.02
0.02 ± .01
0.07 ± .01
0.24 ± .02
5.6 ± .3
0.020 ± .003
0.255 ± .015
0.275 ± .016
0.124 ± .013
< 0.01
< 0.1
< 0.02
0.023 ± .005
< 0.01
< 0.03
< 0.03
< 0.05
< 0.04
< 0.04
< 0.04
< 0.04
< 0.03
0.141 ± .012
(< 0.1)
1134
aNone of the bulk material sampled passed through the 400 mesh (38 pm)
sieve. The material in the size range from 38 pm to 78 un was
resuspended atid sampled with a low-volume TSP sampler.
bTl * 0.1%
27
-------
Table 8
Percent Elemental Composition of High Arsenic Bulk Samples
Element
Al
SI
S
Cl
K
Ca
Tl
V
Cr
Mn
Fe
Ml
Cu
Zn
Aa
Se
Br
Rb
Sr
Y
Ho
Ag
Cd
In
Sn
Sb
Te
Hg
Pb
Bi
Mass (l>g)
F/C
S02Cottrell Dust
Coarse
CB290
< 0.5
< 1.0
< 2.0
< 0.4
< 0.2
< 0.6
< 0.05
< 0.02
< 0.01
< 0.01
0.3 * .1
< 0.05
0.30 * 0.05
1.4 ± 0.1
26 ±3
< 0.1
< 0.4
< 0.1
< 0. 1
< 0.1
< 0.05
< 0.05
< 0.05
< 0.05
< 0.6
0.89 * 0.06
< 0.06
< 0.2
37 ±3
(4 ± 1)
778
0.033
Fine
FB291
< 0.5
< 1.0
< 2.0
< 0.4
< 0.2
< 0.6
< 0.1
< 0.02
< 0.01
< 0.02
0.36 ± .06
0.042 t 0.014
0.28 i 0.04
0.95 ± 0.13
8.6 * 1.5
< 0.1
< 0.5
< 0.04
< 0.05
< 0.06
< 0.1
< 0.2
< 0.2
< 0.3
1.0 ± 0.2
1.0 ±0.2
< 0.3
0.06 t .03
28 ±3
(4 * 1)
59
0.033
No. 1 Flue Dust
Coarse
CB298
< 0.8
< 1.0
< 2.0
< 0.4
< 0.4
< 0.5
< 0.07
< 0.02
0.02 .01
0.02 .01
2.9 0.2
0.05
5.8 .3
1.9 0.1
36 3
< 0.1
< 0.4
< 0,1
< 0.1
< 0.1
0.145 i .011
< 0.05
< 0.05
< 0.05
< 0.06
1.14 ± .08
< 0.06
< 0.2
6.5 ± .3
(1.2 ± .4)
1578
0.016
Fine
FB299
< 0.5
< 2.0
< 3.0
< 0.5
< 0.4
< 0.8
< 0.1
< 0.05
< 0.07
< 0.04
1.08 i .25
< 0.05
4.0 ± 0.8
1.7 ± 0.4
31 ±6
< 0.1
< 0.5
< 0.1
< 0.1
< 0.1
< 0.2
< 0.4
< 0.4
< 0.5
< 0.5
2.4 i 0.1
< 0.6
< 0.2
7.5 i 1.5
(< 1)
26
0.016
As Baghouse Fad
Coarse
CB300
< 1.0
< 1.0
< 0.5
< 0.1
< 0.2
< 0.4
< 0.05
0.012 ± .005
0.016 ± .010
0.02 ± .01
2.11 * .10
< 0.04
2.11 ± .10
0.38 ± .02
64 ±3
0.11 ± .02
< 0.3
< 0.05
< 0.02
< 0.05
0.03 ± .01
< 0-05
< 0.05
< 0.06
< 0.07
2.15 ± .15
< 0.1
0.21 ± .03
1.26 ± .06
(0.04 * .01)
2361
0.034
Fine
FB301
< 1.0
< 2.0
< 2.0
< 0-2
< 0.5
< 0.3
< 0.05
< 0.02
< 0.02
< 0.02
2.2 i .2
< 0.04
2.5 ±0.2
0.52 t .04
26 ±2
0.22 ± 0.02
< 0.4
< 0.1
< 0.1
< 0.1
0.05 ± 0.02
< 0.2
< 0.2
< 0.2
< 0.2
6.8 t .6
< 0.2
0.15 + 0.03
0.20 ± .04
(1.2 t .2)
195
0.034
As Plant Product
Coarse
CB302
< 1.0
< 1.0
0.8 t .2
< 0.1
< 0.1
< 0.2
< .05
< .01
< .01
< .01
2.67 .15
0.03 .01
3.6 .2
0.41 .02
51 3
0.18 .02
< 0.3
< 0.05
< 0.05
< 0.05
0.03 ± .01
< 0.02
0.04 t .01
< 0.02
< 0.02
2.14 ± .15
< 0.02
0.19 * 0.04
1.97 ± .08
(0.5 ± 0.1)
551
0.055
Fine
FB303
< 1.0
< 1.0
< 1.0
< 0.2
< 0.3
< 0.2
< 0.05
< 0.02
< 0.02
< 0.02
0.54 .20
0.02
0.38 .12
0.14 0.06
4.0 1.0
< 0.3
< 0.3
< 0.05
< 0.05
< 0.05
< 0.3
< 0.4
< 0.4
< 0.5
< 0.5
< 0.6
< 0.6
< 0.3
0.50 ± .24
( < 1)
19
0.055
CO
-------
Table 9
Percent Elemental Composition of Settled Dust
Collected Within the Plant and the Ore Concentrate
Element
Al
Si
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Nl
Cu
Zn
As
Se
Br
Rb
Sr
Y
Mo
Ag
Cd
In
Sn
Sb
Te
Hg
Pb
Bl
MasB(vs)
F/C
Roadway Dust by FOB
Coarse
CB284
2.5 + 0.2
6.4 ± 0.3
11.8 ± 0.7
< 0.1
0.42 * 0.02
0.72 ± .05
0.20 * 0.02
< 0.04
0.060 t .010
0.076 t .008
17.9 * 0.9
< 0.05
18.7 i 1.0
1.12 ± .09
1.99 ± .15
< 0.02
< 0.1
< 0.01
0,014 * .005
< 0.01
0.32 ± 0.02
0.052 t .010
0.08 * .03
< 0.04
< 0.04
0.068 ± .028
< 0.04
< 0.02
1.82 ± .10
(0.4 l 0.1)
1023
0.10
Fine
FB285
5.9 ± 0.6
12.1 ±1.2
12.5 ± 1.2
< 0.1
0.92 t .09
0.87 + .09
0.26 * .03
< 0.03
0.083 ± .025
0.081 i .015
17.3 ± 1.5
< 0.1
17.2 t 1.5
1.4 ± .2
4.4 ± .5
< 0.02
< 0.1
< 0.02
< 0.02
< 0.02
0.36 ± -06
< 0.1
< 0.2
< 0.2
< 0.2
0.3 t 0.2
< 0.1
< 0.05
4.6 t 0.4
(0.7 1 .2)
136
0.10
Roadway Dust by Sample Bldg.
Coarse
CB286b
2.4 t 0.2
7.4 ± 0.4
6.9 ± 0.4
< 0.1
0.31 * 0.2
3.4 ± 0.2
0.18 t .01
< 0.03
0.054 ± .009
0.064 ± .008
11.8 0.6
0.1
13.0 0.7
1.05 .08
2.4 0.2
< 0.02
< 0.1
< 0.02
< 0.02
< 0.02
0.16 ± .02
0.08 ± .02
0.10 * .03
< 0.05
0.08 ± .02
0.15 t 0.05
< 0.1
< 0.05
3.1 1 0.2
(0.6 t .2)
1015
0.097
Fine
FB287C
4.4 ± .4
11.4 t 1.2
6.9 ± 0.8
< 0.1
0.60 t .07
4.9 * .5
0.22 * .03
< 0.03
0-057 ± .010
0.084 ± .012
11.1 ± 1.0
0.11 ± 0.03
12.5 ± 1.2
1.47 ± .14
3.9 * 0.5
< 0.4
< 0.2
< 0-02
< 0.02
< 0.03
0.11 ± .05
0.14 ± .06
< 0.15
< 0.1
< 0.1
< 0.1
< 0.1
< 0.05
5.8 ± .6
(0.7 * 0.2)
137
0.097
Martin Mill Weighing Floor
Coarse
CB292
2.0 ± 0.2
7.6 * 0.4
13.4 ± 0.7
< 0.1
0.32 t .02
1.39 ± .08
0.18 ± .03
< 0.03
0.04 ± .01
0.048 t .006
10.9 ±0.6
< 0.1
18.2 * 1.0
0.56 ± .08
6.9 * .4
0.04 * .01
< 0.2
< 0.02
0.023 1 .005
< .03
< 0.05
0.06 ± .01
< 0.03
< 0.03
0.07 t .03
0.22 i .03
< 0.04
< 0.05
0.39 ± .02
(0.16 1 .04)
1179
0.019
Fine
FB293
5.5 ± 0.6
14.1 t 1.5
13.2 i 1.4
< 0.1
0.71 t .08
1.00 1 .10
0.18 ± .03
< 0.03
0.05 ± .01
0.05 ± .01
8.0 ± .8
< 0.1
15.8 + 1.5
0.37 t .07
6.5 ± .6
< 0.4
< 0.2
< 0.02
0.04 i 0.02
< 0.03
< 0-08
< 0.2
< 0.2
< 0.2
< 0.2
< 0.2
< 0.2
< 0.1
1.07 t .12
(< 0.3)
124
0.019
Lepanto Copper Concentrate
Total
LB333
0.27 * 0
1.47 ± 0.08
7.8 + 0.4
< 0.1
0.11 t .01
0.36 ± 0.02
0.12 ± 0.01
< 0.03
0.025 ± 0.10
0.036 t .15
13.2 ± 0. 7
< 0.1
17.7 1 0.9
0.55 ± 0.07
7.0 t 0.4
0.04 ± .01
< 0.2
< 0.02
< 0.02
< 0.03
< 0.05
< 0.04
< 0.05
< 0.05
< 0.05
0.20 1 0.04
< 0.05
< 0.05
0.21 * 0.02
(0.07 * 0.01)
1336
-
to
\o
"None of the bulk sample pnased through the 400 me ah ( < 38 un) sieve.
reauapended and sampled with a low-volume TSP sampler.
Particles In the size range from 38 um to 78 urn was
DT1 *
CT1
O.U
0.3t
-------
Table 10
Percent Elemental Composition of Emission from Number 1
Brick Flue: Fine Fraction ( < 2.5 vim)
Element
Al
Si
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Y
Mo
Ag
Cd
In
Sn
Sb
Te
Hg
Pb
Bi
Mass (yg)
F/C
Sample Identification
MF875
< 0.2
< 0.2
< 2.0
< 0.3
< 0.5
< 0.8
< 0.1
.015 ± .003
.013 ± 0.003
< 0.01
0.28 ± .02
0.03 ± .01
2.8 ± 0.2
3.2 ±0.2
21.2 ±1.8
< 0.1
< 0.3
< 0.1
< 0.1
< 0.1
0.11 ± .02
0.13 ± .04
0.72 ± .24
0.02 ± .01
0.34 ± .12
6.6 ± 0.6
0.20 ± .05
0.2 ± 0.1
18.6 ±1.0
0.5 ± 0.1
6269
5.5
MF877
< 0.2
< 0.2
< 2.0
< 0.3
< 0.5
< 0.8
< 0.1
0.016 ± .005
0.014 ± .004
0.010 ± .005
0.28 ± 0.02
0.04 ± .01
3.2 ± 0.2
3.7 ± 0.3
26.4 ± 1.7
< 0.1
< 0.3
< 0.1
< 0.1
< 0.1
0.09 ± .02
0.11 ± .02
0.40 ± .05
0.02 ± .01
0.48 ± .10
6.0 ± .4
0.16 ± .04
< 0.2
17.9 ± 0.9
0.4 ± 0.1
5655
8.4
MF879
< 0.2
< 0.2
< 2.0
< 0.3
< 0.5
< 0.8
< 0.1
0.016 ± .006
0.015 ± .004
0.014 ± .008
0.32 ± 0.02
0.05 ± .02
2.7 ± .2
6.3 ± .3
26.4 ± 1.7
< 0.1
< 0.3
< 0.1
< 0.1
< 0.1
0.11 ± .02
0.13 ± .03
0.40 ± .08
0.03 ± .01
0.42 ± .09
6.1 ± .5
0.16 ± .04
< 0.2
17.-6 ±0.9
0.3 ± 0.1
6225
8.1
MF883
< 0.2
< 0.2
< 2.0
< 0.3
< 0.5
< 0.8
< 0.1
0.016 ± .003
0.014 ± .003
< 0.01
0.28 ± 0.02
0.04 ± .02
3.2 ± .2
3.7 ± .2
23.1 ± 1.6
< 0.1
< 0.3
< 0.1
< 0.1
< 0.1
0.09 ± 0.02
0.10 i .02
0.53 ± .07
0.02 ± .01
0.36 ± .09
5.2 ± 0.4
0.14 ± .04
< 0.2
19.7 ± 1.0
0.4 ± 0.1
6358
7.6
Mean ± SD
< 0.2
< 0.2
< 2.0
< 0.3
< 0.5
< 0.8
< 0.1
0.016 ± 0.00050
0.014 ± 0.00082
0.0085 ± 0.0044
0.29 ± 0.020
0.040 ± 0.0082
3.0 ± 0.26
4.2 ± 1.4
24 ±2.6
< 0.1
< 0.3
< 0.1
< 0.1
< 0.1
0.10 ± 0.012
0.12 ± 0.015
0.51 ± 0.15
0.023 ± 0.0050
0.40 ± 0.063
6.0 ± 0.58
0.17 ± 0.025
< 0.2
18 ± 0.93
0.40 ± 0.082
7.2a
values included
-------
Table 11
Percent Elemental Composition of Emission from Number 4 Converter
Secondary Hood: Fine Fraction ( < 2.5 ym)
Element
Al
Si
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Y
Mo
Ag
Cd
In
Sn
Sb
Te
Hg
Pb
Bi
Mass (gg)
F/C
MF869
< 0.2
< 0.2
< 2
< 0.2
< 0.2
< 0.2
< 0.02
< 0.01
< 0.01
< 0.01
0.135 ± .010
< 0.02
0.41 ± .02
1.06 ± .08
18.7 ±1.5
< 0.2
< 0.2
< 0.04
< 0.02
< 0.04
0.04 ± 0.02
0.08 ± .03
0.20 ± .06
< 0.01
0.22 ± 0.08
1.39 ± 0.34
0.12 ± 0.04
< 0.13
17.8 ± .9
2.4 ± 0.1
2121
910
Sample Identification
MF897
< 0.2
< 0.2
< 2
< 0.2
< 0.2
< 0.5
< 0.2
< 0.03
< 0.03
< 0.03
0.50 ± 0.15
< 0.03
0.53 ± 0.10
1.76 ± .25
41 ±6
< 0.2
< 0.2
< 0.08
< 0.06
< 0.15
< 0.15
0.8 ±0.3
1.9 ± 0.4
0.30 ± 0.10
0.96 ± 0.30
4.1 ± 0.9
< 0.06
< 0.4
21.5 ± 2.6
0.6 ± 0.2
93
6.8
MF895
< 0.2
< 0.2
< 2
< 0.2
< 0.2
< 0.4
< 0.1
< 0.03
< 0.02
0.02 ± 0.01
0.33 ± 0.05
0.04 ± 0.01
2.8 ± 0.2
6.4 ± 0.4
17.8 ± 2.6
< 0.2
< 0.3
< 0.06
< 0.05
< 0.09
< 0.15
0.12 ± 0.04
< 0.1
0.10 ± .06
0.60 ± .15
1.31 ± 0.30
0,06 ± 0.03
< 0.2
46 ±3
0.6 ± 0.1
3774
56
MF865
< 0.2
< 0.2
< 2
< 0.2
< 0.2
< 0.6
< 0.05
< 0.01
< 0.02
< 0.02
.08 ± .02
< .04
0.66 ± .05
1.09 ± .08
29 ±2
< 0.2
< 0.2
< 0.06
< 0.06
< 0.10
< 0.04
0.04 ± .03
0.25 ± .08
0.16 ± .07
0.36 ± .10
5.8 ± .09
0.08 ± .06
< 0.2
27.5 ± 1.5
0.5 ± 0.1
8468
47
Mean ± SD
< 0.2
< 0.2
< 2
< 0.2
< 0.2
< 0.4
< 0.1
< 0.02
< 0.02
< 0/02
0.26 ± 0.19
< 0.03
1.1 ±1.1
2.6 ± 2.6
26 ±. 11
< 0.2
< 0.2
< 0.06
< 0.05
< 0.09
< 0.1
0.26 ± 0.36
0.60 ± 0.87
0.14 ± 0.12
0.53 ± 0.32
3.1 t 2.2
0.072 ± 0.038
< 0.2
28 ± 13
1.0 ± 0.92
254 ± 437
-------
Table 12
Percent Elemental Composition of Emission from Reverbatory Furnace
Slag Skim: Fine Fraction ( < 2.5 ym)
Element
Al
Si
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Y
Mo
Ag
Cd
In
Sn
Sb
Te
Hg
Pb
Bi
Mass (fjg)
F/C
Sample Identification
MF893
< 0. 2
< 0.2
< 0.8
< 0.2
< 0.5
< 0.5
< 0.02
< 0.01
< 0.01
< 0.01
0.21 ± .02
< 0.03
0.15 ± .02
1.8 ± .1
55 ±4
< 0.2
< 0.2
< 0.1
< 0.1
< 0.1
0.09 ± .01
0.010 ± .003
0.61 ± .07
0.013 ± .003
0.15 ± .04
1.11 ± .15
0.071 ± .017
< 0.3
5.2 ± .3
0.26 ± 0.05
21,779
25
MF863
< 0.2
< 0.2
< 1.0
< 0.2
< 0.5
< 0.5
< 0.03
< 0.01
< 0.01
< 0.01
0.17 ± .01
< 0.03
0.15 ± .02
1.7 ± .1
62 ±5
< 0.2
< 0.2
< 0.1
< 0.1
< 0.1
0.09 ± .01
0.012 ± .003
0.69 ± .08
0.012 ± .003
0.14 ± .03
1.06 ± .14
0.092 ± .15
< 0.3
6.8 ± .4
0.50 ± .08
9242
30
MF867
< 0.2
< 0.2
< 1.0
< 0.2
< 0.5
< 0.5
< 0.02
< 0.01
< 0.01
< 0.01
0.19 ± .02
< 0.03
0.15 ± .01
1.7 ± .1
60 ±5
< 0.2
< 0.2
< 0.1
< 0.1
< 0.1
0.10 ± .01
0.010 ± .003
0.77 ± .09
0.011 ± .003
0.11 ± .03
1.07 ± .12
0.12 ± .02
< 0.3
5.4 + .3
0.32 ± .05
13,733
25
MF871
< 0.2
< 0.2
< 1.0
< 0.2
< 0.5
< 0.5
< 0.03
< 0.01
< 0.01
< 0.01
0.19 ± .02
< 0.03
0.16 -± .02
1.7 ± .1
63 ±5
< 0.2
< 0.2
< 0.1
< 0.1
< 0.1
0.09 ± .01
0.013 ± .003
0.76 ± .06
0.013 ± .003
0.11 ± .03
1.04 ± .11
0.076 ± .015
< 0.3
6.5 ± 0.4
0.39 ± .05
10,044
27
Mean ± SO
< 0.2
< 0.2
< 1.0
< 0.2
< 0.5
< 0.5
< 0.3
< 0.01
< 0.01
< 0.01
0.19 ± 0.016
< 0.03
0,15 ± 0.0050
1.7 ± 0.050
60 ± 3.6
< 0.2
< 0.2
< 0.1
< 0.1
< 0.1
0.092 ± 0.0050
0.011 ± 0.0015
0.63 ± 0.22
0.012 ± 0.00096
0.13 ± 0.021
1.1 ± 0.029
0.090 ± 0.022
< 0.3
6.0 ± 0.79
0.37 ± 0.10
26.8
-------
Table 13
Percent Elemental Composition of Emissions from Number 1
Brick Flue: Coarse Fraction ( > 2.5 um)a
Element
Al
Si
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Y
Mo
Ag*
Cd*
In*
Sn*
Sb*
Te*
Hg
Pb
Bi
Mass (yg)a
F/C
Sample Identification
MC876
< 2
< I
< 2
< 0.5
< 0.5
< 2
< 0.2
0.016 ± .005
0.021 ± .005
0.011 ± .005
0.34 ± .05
0.06 ± .02
2.1 ± 0.2
2.3 ± 0.3
46 ±5
< 0.2
< 1.0
< 0.1
< 0.1
< 0.1
0.116 ± .014
0.10 ± .03
0.58 t .11
0.02 ± .01
0.31 t .05
5.3 ± .4
0.20 ± .06
< 0.5
12.6 ± 1.6
-
1142
5.5
MC878
< 2
< 1
< 2
< 0.5
< 0.5
< 2
< 0.2
0.010 ± .007
0.020 ± .005
0.013 ± .005
0.48 ± .06
0.10 ± .02
2.3 ± .3
5.0 ± .7
37 ±4
< 0.2
< 1.0
< 0.1
< 0.1
< 0.1
0.185 ± .033
0.10 ± .03
0.40 ± .10
< 0.02
0.37 ± .05
5.7 ± .4
0.14 ± .06
< 0.5
16 ±2
-
674
8.4
MC880
< 2
< 1
< 2
< 0.5
< 0.5
< 2
< 0.2
0.018 ± .06
0.011 ± .06
0.018 ± .05
0.40 ± .06
0.06 ± .02
2.6 ± .3
5.2 ± .1
42 ±5
< 0.2
< 1.0
< 0.1
< 0.1
< 0.1
0.122 ± .029
0.19 t .03
0.41 ± .10
< 0.02
0.48 ± .07
6.1 ± .5
0.21 ± .07
< 0.5
17 ±2
-
768
8.1
MC884
< 2
< 1
< 2
< 0.5
< 0.5
< 2
< 0.2
0.015 ± .005
0.015 ± .005
0.016 ± .006
0.37 ± .04
0.07 ± .01
2.6 ± .3
3.5 ± .4
25 ±4
< 0.2
< 1.0
< 0.1
< 0.1
< 0.1
0.111 ± .017
0.09 ± .03
0.44 ± .10
< 0.02
0.38 ± .05
5.2 ± .5
0.18 ± .06
< 0,5
17 ±2
-
832
7.6
Mean ± SD
< 2
< 1
< 2
< 0.5
< 0.5
< 2
< 0.2
0.015 ± 0.003
0.017 ± 0.005
0.015 ± 0.003
0.40 ± 0.06
0.07 ± 0.02
2.4 ± 0.2
4.0 ± 1.4
38 ±9
< 0.2
< 1.0
< 0.1
< 0.1
< 0.1
0.134 ± 0.035
0.12 ± 0.05
0.46 ± 0-08
< 0.-02
0.39 ± 0.07
5.6 ± 0.4
0.18 ± 0.03
< 0.5
16 ±2
-
-
7.2b
aBased on mass and elemental composition after subtracting fine particles mass deposited with coarse particles.
*Fine fraction not subtracted.
bAII values included in averatze.
-------
Table 14
Percent Elemental Composition of Emissions from the Number 4
Converter Secondary Hood: Coarse Fraction ( > 2.5 vo)a
Element
Al
Si
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
y
Mo
Ag*
Cd*
In*
Sn*
Sb*
Te*
Hg
Pb
Bi
Mass (yg)a
F/C
Sample Identification
MC896
< 3
< 10
< 30
< 5
< 5
< 2
< 0.2
< 0.05
< 0.05
-
7.1 ± 1.2
-
9.4 ± 1.5
< 2
< 22
< 0.4
< 1.0
< 0.3
< 0.2
< 2.0
< 0.2
0.16 ± .04
0.26 ± .05
< 0.05
0.42 ± .08
1.73 i .3
< 0.02
< 0
< 20
-
68
56
MC872
-
-
-
-
-
-
-
-
-
-
-
-
-
_
_
_
-
-
-
-
0.38 ± 0.09
0.17 ± 0.09
< 0.05
< 0.05
1.74 + .3
0.4 ± 0.1
-
-
-
13.7
6.8
aNet deposit after subtracting fine particles deposited
with coarse fraction.
*Fine fraction not subtracted.
34
-------
Table 15
Percent Elemental Composition of Emissions from Reverbatory
Furnace Slag Skim: Coarse Fraction ( > 2.5 ym)
Element
Al
Si
S
Cl
K
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Se
Br
Rb
Sr
Y
Mo
Ag
Cd
In
Sn
Sb
Te
Hg
Pb
Bi
Mass (iJg)
F/C
Sample Identification
MC864
< 1.0
< 1.0
< 5
< 0.5
< 2
< 0.8
< 0.06
< 0.02
< 0.01
< 0.02
0.34 ± .06
< 0.05
0.175 ± .05
0.88 ± 0.45
69 ± 18
< 0.3
< 0.9
< 0.3
< 0.05
< 0.2
0.10 ± .04
< 0.03
0.85 ± .19
0.023 ± .015
0.19 ± .08
0.92 ± .15
0.14 ± .07
< 0.5
8.2 ± 2.0
-
303
30
MC870
< 1.0
< 1.0
< 5
< 0.5
< 2
< 0.8
< 0.06
< 0.02
< 0.01
< 0.03
0.42 ± .07
< 0.05
0.158 ± .04
0.75 ± .39
72 ± 16
< 0.3
< 0.9
< 0.3
< 0.05
< 0.2
0.115 ± .03
< 0.03
0.20 ± .08
< 0.02
0.22 ± 0.10
1.04 ± 0.18
0.12 ± 0.08
< 0.5
7.7 ± 1.9
-
374
27
Mean ± SD
< 1.0
< 1.0
< 5
< 0.5
< 2
< 0.8
< 0.06
< 0.02
< 0.01
< 0.03
0.38 ± 0.06
< 0.05
0.167 ± 0.01
0.82 ± 0.09
71 ±2
< 0.3
< 0.9
< 0.3
< 0.05
< 0.2
0.11 ± 0.01
< 0.03
0.53 ± 0.46
< 0.02
0.21 ± 0.02
0.98 ± 0.08
0.13 ± 0.01
< 0.5
8.0 ± 0,4
-
35
-------
Table 16
Comparison of Elemental Composition of Slag
This Study
Coarse Fraction
Al
Si
Ca
Ti
V
Cr
Mn
Fe
Ni
Cu
Zn
As
Sr
Mo
Ag
Cd
Sn
Sb
Hg
Pb
1.8
14.1
3.6
0.30
0.04
0.11
*
0.13
21.6
0.041
1.96
1.74
1.88
0.019
0.100
0.05
0.19
0.89
± 0.2
± 0.9
± 0.2
± 0.05
± 0.01
± 0.02
± 0.02
± 1.4
± .005
± 0.10
± 0.09
± 0.12
± 0.004
± 0.015
< 0.02
± 0.01
< 0.06
± 0.03
< 0.03
± 0.08
Reference No. 7
(a)
2.2
13.
3.9
0.17
0.004
0.068
0.15
42. (oxide?)
0.008
0.098
1.3
0.17
0.096
0.0006
< 0.03
0.038
0.35
< 0.09
0.18
(a)
(b)
Semiquantitative spectrographic analysis
Atomic absorption analysis
36
-------
Table 17
Elemental Concentration of Ambient Samples (ug/m3)*
Element
Fe
Cw
As
Se
Br
Ag
Cd
In
Sn
Sb
Te
Hg
Pb
Site
Date
Sample No.
181714
1.2 ± .2
1.25 ± .15
2.5 t .3
0.039 i .008
-
0.02 ± 0.01
< 0.03
< 0.03
< 0.03
0.12 ± 0.06
< .06
0.015 ± .010
0.70 ± .08
P14
9/18/83
131322
1.9 ± .2
2.1 ± .2
2.4 1 .3
0.04 ± .01
-
0.016 i .008
< 0.03
< .03
0.11 i .03
0.12 ± .04
< .05
0.02 ± .01
1.65 ± .15
P14
1/13/84
131369
1.7 1 .2
0.32 i .03
0.20 * .05
< .005
0.12 i .06
< .03
< .03
< .03
< .03
0.06 ± .04
-
< .01
0.53 i .05
P14
1/18/84
131382
1.9 ± .2
1.1 ± .1
0.51 t .08
< .005
-
< .03
< .03
< .03
< .03
< .03
-
< .01
0.95 ± .09
P14
1/19/84
131310
0.93 ± .15
0.51 ± .06
0.34 ± .06
< .005
-
< .03
< .03
<
< .02
0-06 ± .04
-
< 0.01
0.16 ± .02
P15
1/13/84
181753
1.2 * .2
2.2 ± .2
2.9 i .3
0.023 ± .006
-
0.04 1 .02
0.03 ± .02
<
0.08 ± .03
0-20 ± .06
-
0.03 + .01
1.36 ± .15
P14
9/21/83
182120
0.96 ± .10
0.48 l .05
1.6 ± .2
0.029 ± .006
-
< .03
< .03
<
< .03 '
0.07 ± .04
-
< .01
0.63 ± .08
P15
11/8/83
182488
0.64 + .09
0.21 1 -03
0.21 ± .04
< .005
-
< .03
< .03
<
< .03
< .04
-
< .01
0.31 ± .04
P2
12/20/83
182256
2.6 i .5
2.6 i 0.2
2.8 i .3
0.08 i .02
-
0.02 i .01
< .01
<
0.03 ± .01
0.16 i .03
< .03
0.025 i .010
1.62 ± 0.15
P14
12/20/83
182'. <)>
0.66 t .09
0.22 1 .03
0.21 i .04
< .005
-
< .03
< .03
* .03
< .03
< .04
-
< .01
0.29 1 .03
P15
12/20/83
*S, Cl, K, Ca, Ti, V, Cr, Mn, Ni, Zn,,Ca, Rb, Sr, Y, Zr, Mo, Pd, Ba, and La were also measured, but the results were not substantially different
from the blank or there were substantial potential interferences from the glass fiber impurities.
-------
Table 18a
Correlation Matrix
(10 Ambient glass fiber filters)
Element
Cu
As
Sb
Pb
Fe
0.71
0.45
0.40
0.77
Cu
0.88
0.86
0.95
As Sb
0.92
0.81 0.75
Table 18b
Slope Matrix
(10 Ambient glass fiber filters)
Element
Cu
As
Sb
Pb
Fe
0.50
0.24
4.39
0.89
Cu
0.67
13.60
1.55
As Sb
18.99
1.73 0.078
Table 18c
Intercept Matrix
(10 Ambient glass fiber filters)
Element
Cu
As
Sb
Pb
Fe
0.82
1.04
0.97
0.64
Cu
0.18
-0.12
-0.17
As Sb
-0.34
-0.053 0.026
-------
Table 19
Ti'/f iJir.Q RESLU TS FDR CHB # MB338
VC'TAL SIZE FRACTION
5JTE: Pi 4
SAMPLING DAlEr 83 958 SITE CODF: 6
SAMPLING DURATION: 24 HRB. WITH START HOUR: 0
EFFECTIVE VARIANCE FITTING. REDUCED CHI SDUARE:
(SOURCE) (I.IG/M3)
1 SL.BBI-h * 2.902+- .649
2 BRKFLU * 2.994+- .534
(PERCENT)-
6.749+- 1.546
6.962+- 1.288
185 D OF F:
TOTAL
895-1
13.710+- 2.067
t. or r
1
|
4
5
LJ i. C
Fe
Cu
As
Sb
Pb
*
*
*
*
*
1 .£00+
1 . 250+
2. 500+-
. 120+-
. 700+-
. 200
. 150
. 300
. 06O
. 080
2.791
2.907
5.814
.279
1 . 628
.014+-
. 094+ -
2. 460+-
.212-*-
.713+--
.001
. OO8
. 1 30
.017
. 036
XKH 1 IL.U
-O12+-
. O75+-
.984+-
1 .763+-
1 .019+-
. 002
.011
. 129
. 893
. 127
Fe
Cu
As
Sb
Pb
MEAS. AMB. MASS (UG/M3): 43.0
* - FITTING SOURCE OR ELEMENT
Table 20
CMBDEQ RESULTS FOR CMB * MB338
TOTAl SIZE FRACTION
SITE: PJ.4
SAMPLING DATE; 83 918 SITE CODE: 6
SAMPLING DURATION: 24 MRS. WITH START HOUR: 0
EFFECTIVE VARIANCE FITTING. REDUCED CHI SQUARE:
. 064 D OF F: 2
1
2
7
SLGSKM
BRKFLU
RDBLDG
*
*
*
3.
1.
9.
l
1
5
37 + -
26 + -
64 + - 1
. 646
. 563
. O45
7.
2.
22.
irtKUt
760+-
618+-
243+-
LIM i
1 .
1.
2.
550
315
661
TOTAL:
14.027+- 1.351
32.620H
(SPECIE) (MEAS. UG/M3) ('/.) (CALC. UG/M3)
1 Fe * 1.2OO+-- .200 2.791 1.138+- . O57
2 Cu * 1.250+ .150 2.907 1.2B2+- .067
3 As * 2.500+- .300 5.814 2.502+- .125
4 Sb * .120+- .060 .279 .119+- .OO8
5 Pb * .700+- .080 1.628 .699+- .034
--(RATIO)
.949 + - . I6r. Fe
1.026+- .134 Cu
130 A«
499
.001+-
.988+-
.999+-
. 124
Sb
Pb
MfcAS. AMB. MASS (U6/M3): 43.0
* - FITTING SOURCE OR ELEMENT
39
-------
Table 21
CMRDED RESULTS FDR Crtfl # MB338
TOTAL SIZE FRACTION
SITE; P14
SAMPLING DATE: 83 9] 8 SITF CODE: 6
SAMPLING DURATION: 24 HRS. WITH START HOUR: 0
EFFECTIVE VARIANCE FITTING. REDUCED CHJ SPJJARF
.055 D OF F
7
6
14
I ou(Jru_K. ,'
HOOD *
RDFOB *
ASPL.NT *
UJb/no;
d . 788+-
5.96J+-
3.715+-
.886
.712
.894
l^tKL-tltJ 1 )
4. 158+- 2. O70
' 13.862+- 1.789
8.639+- 2. 122
TOTAL:
1i.464+- 1.446
26. 66C-+- 3. 606
(SPECIE)
j
2
~';
4
ffi
Fe?
Cu
As
Sb
Pb
*
#
K
#
*
-- ; HEAS . UG/M3 ) ( '/. )
1 .
.1 .
. ^
a
2OO+-
250 + -
500+ -
120+-
700+-
. 200
. 1 50
. 300
. Oi?0
. 080
2.
p
5.
m
1.
791
907
814
279
628
vCnLC. LtG/
1 .
1 .
j''
m
171+-
26B+-
47 8-1- -
1 39+-
682+--
M3>
. O54
. 063
.226
. 040
. 233
(RATIO)-
. 976-1 -
1.014+--
.991+
1. 158+-
. 975+-
. J fr9
. 132
. 149
. 6.S7
. 350
Fp
Cu
AT,
Sb
Pb
MEAS. AMB. MASS (UG/M3): 43.0
* - FITTING SOURCE OR ELEMENT
40
-------
'' -ULTS i- !JP I ' : ' '
r:i/.E FR ACT I ON
, I 4
'iNG DATE: S3 918 SITE CODE: 6
ING DURATION: 24 MRS. WITH START
CTIVE VARIANCE FITTING. REDUCED CHI SPUARF
.94} !.'
'. SOURCE")-- -i.lS/M"
BRKFLU * -?. -.09 :--
RDF OB * 5. '371+ -
ASPL.NT * 3.?6c. '
TOTAL: 11
CIE) - ,i->:"-.:. '- /i.:".
rfr * i . .. -
Cu * -, . . -. . - . :! -;
A '-5 * ": . ,' :, . 3OO
:h * . ! ..O-i . 060
. b * -. ->- - . 080
------ . i: EROtN r
.'<.' 6. uo7-t - 1 .
, ".'- -. ;. . 700-1 t .
:- ' . s94-h 1 .
,-'-". t 1 f
, '. - .,,__!. i||3/
, , '.) i . 149 + -
...'.907 1.29"+-
5.814 2.4O9+-
,279 .230+-
1.628 .641+-
; -
i .:'. J-
" 7^.-
.->82
^1
M,J:
. <:;'-:i3
. C.'iO
1 J 9
. 0 1 6
. O25
.^W<. i ,'.;j (UB/M3) : 43.0
FITTING SOURCE OR ELEMENT
Table 23
HMPDEQ R'ESUI T3 FOR CMB « MB335
TOT A'.. B I :; t FRrtCT I ON
SITF: Fl-1
SA^l^'i riviG :"^i(F: 34 513 S.I i'F IHHE: 6
SAit!-:'L !iv;r. i>!.jRATK]N: 24 HRS. WITH START HOUR: 0
Pi* fr:i.:'i i'/:... VARIANCE FITTING. REDUCED CHJ SQUARE:
002 D OF F
UJG/M3)
* 1,981+- .646
URKFLU *
7 RDKL.D6 *
13 COTTRL *
TOTAL:
15.
-- (PERCENT)
r.^62+- 1.307
: . 767H- 1 .549
1 . .-^V-H- 3. 037
-), ;.;-(i - 1 . 270
-(SPECIE)- (iiir,
1 Fe * 1. -
2 Cu * 2. "> *
3 As * 2- t;
4 Sb * . i
5 Pb * 1 . 61
, I1.-:-
4. 2OO
°t. a oo
. 240
3. 300
::AL.C. UG/M3) (R;1.! in.
l.S93< .096 .9?6+-
2. 1O7 + - . i 12 1 . OO.'-'.H -
2. 4OO +- . 108 1 . 00;, + -
.12O+- .010 .999+-
1.650+- .08O 1.000f-
J 16
i 09
MEAS. AMB. MASS UJG/M3): 50.0
» - FITTING SOURCE OR Et. T MENT
-------
Table 24
CMBDEG RESULTS FOR CMB # MB535
TOT A! SIZE FRACTION
SITE: PI 4
SAMPLING DATE: 84 113 SITE CODE: 6
SAMPLING DURATION: 24 HRS. WITH START HOUR: O
EFFECTIVE VARIANCE FITTING. REDUCED CHI SQUARE:
.072 D OF F: 1
1
6
13
- \ suuivut ) -
SL6SKM
BRKFLU
RDFOB
CCTTRL
TC
(SPECIF
1
2
-?
4
5
Fe
Cu
As
Sb
Pb
3TAI
:. )
-*
*
K
*
*
tuu/rio.)
* 1 . 863+-
* 1.O91+-
* 1O.768+-
* 3.O97+-
..: 16.819+- 3
(ME AS. UG/M3)
1 .
2.
r- »
B
1.
9OOH
1OO+-
4OOn
120+-
650+-
. 200
. 2OO
. 300
. 040
. 1 50
^f- thi_,C.IM 1 1
.655 3.726+- 1.324
.763 2.1B2+- 1.531
.871 21.536+- 2.O49
.636 6. 193+- 1.310
L.47
( y.
3.
4.
4.
m
3.
5 3
800
2OO
800
240
300
3.637+- 3.
(CALC. UG/.
1.
2.
2.
,
1.
943+-
058+-
399 + -
121+-
650+-
396
M-rr %
_' /
. 097
, 1O8
.119
. OO7
. 095
1
1
1
1
(RATIO)
. 023+-
. 980+-
. 000 f-
. 007+ -
. 000+
.119
. 107
. 134
.345
. lOEi
Fe
Cu
Ac
Sb
Pb
MEAS. AMB. MASS (UG/M3): 50.0
* - FITTING SOURCE OR ELEMENT
Table 25
CMBDEQ RESULTS FOR CMB # MB336
TOTAL SIZE FRACTION
SITE: P14
SAMPLING DATE: 84 118 SITE CODE: 6
SAMPLING DURATION: 24 HRS. WITH START HOUR: O
EFFECTIVE VARIANCE FITTING. REDUCED CHI SQUARE
2.967 D OF F
4
7
v s>uun.L,e. > vLJo/no^
HOOD * .817+-
SLGDMP * 6.959+- 1
RDBI..DG * 1 . 352+-
TC
(SPECIE
1
2
3
4
5
Fe
Cu
As
Sb
Pb
3TAL: 9.
i)
*
#
#
*
*
irie.Ha« u
1 . 700+-
. 320+-
. 2OO+-
. O60+-
.530+-
129+- 1
G/M3)
. 200
. 030
.050
. 040
. 050
IftlM-C.!1*! 1 J ~ ' -
.289 .717+- .256
..128 6. 1O5+- 1.O35
.327 1.1 86+- .293
. . 209 1
(./,)
1.491
.281
. 175
. 053
.465
B.008+- I.
(CALC. UG/
1.665+-
.321+-
. 376+-
. O4 1 +-
. 333+-
134
M-T \
O /
.098
.015
. 090
.018
. 1 O6
/ Cf/\T V f~\ \
. 979+-
1.004+-
1.B79+-
.677+-
.628+-
. 129
. 1 05
.652
. 543
. 209
Fe
Cu
As
Sb
Pb
MEAS. AMB. MASS (UG/M3): 114.0
* - FITTING SOURCE OR ELEMENT
-------
Table 26
CMF.DEQ RESULTS FOR CMB # ME337
TOTAL SIZE FRACTION
5.1TE: PI A
SAMPLING DATE: 84 139 S3TE CODE: 6
SAMPLING DURATION: 24 HRS. WITH START HOUR: O
EFFECTIVE VARIANCE FITTING, REDUCED CHI SQUARE:
2.475 D OF F:
(SOURCE)
4 SLGDMP * 3.
A RDFOB * 5.
13 COTTRL * 1.
TOTAL : 11.
(SPFCTF)--
3
2
3
4
5
Fe
Cu
As
Sb
Pb
#
*
*
#
*
(UG/M3)
980+ - 1 . 2O 7
471+- .677
841+- .231
293+- 3
: . 403
( MF AS . UP '|*n ^ -- ' v ^
1.9OO+- .200
1 . 100+-
. 5 10+-
<
, 950 +-
. 1 00
. 000
. 030
. 090
1 . 348
. 780
. 362.
.674
(PERCENT
2.S23+- .
3.880+-
1 . 3O6+-
8. OO9+- 1 .
\ __
867
537
176
072
i'rv^ii r iin; /MT'I
1.845+- .074
1 . 107+-
. 662+-
. 028+-
.816+-
. 055
. 056
. 002
. O56
i' Q A T" T f~l \
*.r\H 1 1U )
.971+- . 109
1 . 006+-
1 . 299+-
. 000+-
. 85?+-
. 1 04
. :?3 1
. 000
. 3 00
Fe
Cu
As
Sb
Pb
MEAS. AME. MASS (UG/M3): 141.O
* ~ FITTING SOURCE.OR ELEMENT
Table 27
CMBDEQ RESULTS FOR CMB * MB33/
TOTAL SIZE FRACTION
SITE: P14
SAMPLING DATE: 84 119 SITE CODE: 6
SAMPLING DURATION: 24 HRS. WITH START HOUR: 0
EFFECTIVE VARIANCE FITTING. REDUCED CHJ SPUARE:
(SOURCE)
3 HOOD *
4 SLGDMP *
6 RDFOB *
(U6/M3)
1.713+- .548
4.264+- 1.219
5.366+- .685
(PERCENT)-
1.215+- .393
3.024+- .878
3.806+- .521
2. 056 D OF F: 2
TOTAL:
11.344+- 1.502
8.045+- 1.137
-(SPECIE)
1
2
3
4
5
MEAS
#
Fe *
Cu *
As *
Sb *
Pb *
. AMB.
(MEAS. UG/M3)-
1.
1.
.
9OO+-
1 OO+-
510+-
<
950+-
. 200
. 100
. OBO
. 030
. 090
MASS (UB/M3):
- FITTING
SOURCE
C/.)
1
141
.348
. 780
. 362
.674
.0
(CALC. UG/
3 .886+-
1 . 106+-
. 632+-
. 065+-
.635+-
M3)
. O77
. 057
. 389
. 038
.223
(RATIO)
. 993+-
1 . 005+-
1 . 240+-
. OOO+-
. .648+-
. 3 12
. 3 05
.438
. 000
.242
Fe
Cu.
As
Sb
Pb
OR ELEMENT
43
-------
Table 28
CMBDFQ RESULTS FOR CMB tt M&334
TOTAL SIZE FRACTION
SITE: PI 5
SAMP!.. ING DATE: 84 i 13 SITE CODE: 7
SAMPLING DURATION: 24 HRE. WITH START HOUR: 0
EFFECTIVE VARIANCE FITTING. REDUCED CHI SQUARE:
.332 D OF F: i
1
T*
4
8
1
V OLMjrXLjCZ. 1
BRKFl. LI
SLGDMP
RD79BT
CALCIN
*
*
*
*
.
1 .
4.
2.
V UO/ 1 !.
489+-
685 + -
328 + -
240 + -
-.,, vr e.r\u.c.i*f i ,1
.
1.
4.
139
431
228
462
1.
5 .
13.
6.
481+-
107 + -
116+-
787 + -
.
4.
12.
1.
428
342
829
440
TOTAL:
8.7424- 4.490 26. 492+-13.666
\ or r. i... i C./
1
2
"^
4
5
Fe?
Cu
As
Sb
Pb
*
*
*
*
*
uinna. uu/rio; \ /.
. 930+-
.510+-
. 3 40+-
. 060+ -
. 1 60+-
. 150
. 060
. 060
. 040
. O20
-.)
1.
1.
.
, ; \UHI_L,. uu/
818
545
030
182
485
. 929+-
. 509+-
. 339+-
. 03 7+-
. 162+-
1 !_/
. 030
.043
. 020
. 003
. 005
v rvH i i u ,'
.999+-
. 998+-
. 996+-
.621+-
1 . 013+-
. 164
. 144
. 185
.41.7
.131
Ft?
Cu
As
Sb
Pb
MEAS. AMB. MASS (UG/M3): 33.0
* - FITTING SOURCE OR ELEMENT
Table 29
CMBDEQ RESULTS FOR CMB # MB334
TOTAL SIZE FRACTION
SITE: P15
SAMPLING DATE: 84 113 SITE CODE: 7
SAMPLING DURATION: 24 MRS. WITH START HOUR: 0
EFFECTIVE VARIANCE FITTING. REDUCED CHI SQUARE:
.677 D OF F
iu(_njru_,i:.; vuu/rlo> vr n.rvu.c.iN i ;
1
2
4
1
BRKFLU
SL6DMP
CALCIN
#
#
#
.
2 m
2 .
571+-
909+-
381+-
. 116
.813
.448
1.
8.
7.
731+-
816+-
2 15+-
.
">
1 .
362
501
402
TOTAL:
862+-
936
17.762+- 2.96
V jDt-tT.U.1 t 1
1
2
3
4
5
Fe
Cu
As
Sb
Pb
*
*
#
*
#
v nc.ua. u»3/rio/ -
. 93 OH
. 5 1 0+-
.340+-
. 060+-
. 160+-
. 1 50
. 060
. 060
. 040
. 020
- <./. ; \L-HI_U. uu/ru;
2.
1.
1.
BIB
545
030
182
485
. 94O+-
.512+-
.280+-
.O42+-
. 170+-
.043
.045
. O2 1
. O03
. O06
1
1
1
\ r\H I i LJ i
.010+-
. 004+-
.823+-
. 694+-
. 065+-
. 169
. 148
. 158
.466
. 139
Fe
Cu
AB
Sb
Pb
MFAS. AMB. MASS (UG/M3): 33.0
* - FITTING SOURCE OR ELEMENT
-------
..,';iii"r.!'' RE'SULTb r-Ul": ''..': -, .'' ".
-i;i: SI >F FRA' f TCJ! :
:'; ! : i: : P 1 4
':-;-iHPi. II-JG DATE: 83 92J £-<.: ' L^: 6
SAMPLING DURATION: 24 HRS. "['" S"! rtRT HOUR: 0
FPTF-.C11VE VARIANCE FITTING. iM.^jruP CHI SQUARE:
Ui'-?/M3)-- ....... ---------- (PERCENT) ----
...'.' 4-I-- 4.633 2/.i, :.:'.93>- 7.500
.'.»-<> -i 4 , 633
26. 394+ - 7 . f:OO
23.151 D Of-'
360+-
. OB2 (--
r,,->i3> c.y (CALC. us. v-tj.- - -
,200 1.935 .O43+- .O31
.200 3.54S .180+- .ISO
.300 4.677 4.255+- 1. BOO 1 . 46~-i-- .:
.060 .323 .507+- .360 2.53/> * - ! . 7"
.150 2.194 4.P.82+- 2.127 3.369: -1. ,'.'.
i.-^'-.s (UG/M3) : 62.0
: j;5 SOURCE OR ELEMENT
Table 31
Ct-IBDEG RESULTS FOR CMB # h ?(
TOTAL SIZE FRACTION
SITE: P14
SAMPLING DATE: 83 921 .-, > ' L
SANi'-i.,Ii-K; OljRATIONs 24 i-iK,:v .
e :>: 't:V.-v i.v..~: VARIANCE FiTTiw::
i;i-:;;)i Ju!.-'0 CHI SQUARE:
2.012 r> or F
....... (ue/h3> ...... -
v 2.8S6+- .879
* 11.582+- 1.29:3
..... (PERCENT) ---------
4.654+- 1.422
18.681+-- 2.13J
10 FLUE
TOTAL.:
* 5.631+- 1
. . 320
20.099+- 2.O46 3.
,: ;r't:-CIi£) (MEAS. U6/M3) -
i
2
3
4
5
Fe
Cu
As
Sb
Pb
#
*
*
*
*
1.
2.
2.
*
1.
200+-
200+-
900+-
2OO+--
360+-
. 2OO
. 200
. 30O
. 060
.150
--(X.)
1 . 935
. 3.548
4.677
.323
2. 194
9. O8P"< - '-- i ">'-
2.41
.196 1 .
.018 1.
. O39
MF.AS. AMB. MASS (UG/M3) : 62. 0
« - FITTING SOURCE OR ELEMENT
-------
Table 32
CUBDED RESUi IS FOR ChP tt MBO37
TOTAL SIZE FRACTION
SITE: P15
SAMPLING DATE: S311 OB SITE CODE: 7
SAMPLING DURATION: 24 HRS. WITH START HOUR: 0
EFFECTIVE VARIANCE FITTING. REDUCED CHI SQUARE:
.131 D OF F: 1
4
10
12
13
SLGDMP
FLUE
CUCONC
COTTRL
*
*
*
*
^T
3
i .
i.
\uo/ no.
224 + -
317+-
25O+-
036+-
/
.584
. 790
.431
. 302
7.
7.
*-)
2.
008+-
21 1+-
717 + -
251+-
1
1
| } __ _ -.-..,.. __
. 318
. 755
. 947
. 66?
TOTAL:
8.826+- 1.115
19.167+- 2.606
-(SPECIE) (MEAS. UB/M3) (7.) (CALC. UG/M3) (RATIO)
1 Fe
2 Cu
3 As
4 Sb
5 Pb
*
*
*
*
*
. 96O+-
. 480+-
1 .600+--
. O7O+-
. 63CH
. 1 00
. O50
. 200
. O40
. O80
2 .
1.
3 .
.
i.
O87
O43
478
152
370
.961+-
.480-*---
1 .61 1+-
. 056+-
. 630+-
. 046
. 0 1 5
. 1 04
. OO 3
. 033
1
1
1
1
. 001+-
. 000+-
. O07+-
. 795+-
, OOOn
. 1 15
. 1 09
. 142
. 456
. 137
Fe
Cu
As
Sb
Pb
MEAS. AMB. MASS (US/M3): 46.0
* - FITTING SOURCE OR ELEMENT
Table 33
CMBDEQ RESULTS FOR CMB « MB037
TOTAL SIZE FRACTION
SITE: PI 5
SAMPLING DATE: 831108 SITE CODE: 7
SAMPLING DURATION: 24 HRS. WITH START HOUR: 0
EFFECTIVE VARIANCE FITTING. REDUCED CHI SQUARE:
.107 D OF F:
_.
J>
4
10
12
-(SOURCE)
HOOD
SLGDMP
FLUE
CUCONC
( UG/M3)
*
*
*
*
1.
3.
o* *
1 .
349 + -
241+-
038+-
273+-
.817
.588
1 . 1 34
.494
2.
7.
6.
2.
(PERCENT)
932+-
046 + -
604 + -
768 + -
1 .
1.
2.
1 .
783
326
4SP>
OB:
TOTAL:
S.901+- 1.595
19.350+- 3.60"
(SPECIE)
1
2
i
4
5
Fe
Cu
As
Sb
Pb
*
*
#
#
#
(MEAS. UB/M3)
. 960-«
. 480+-
1 . 60O+ -
. 070+-
. 630+-
. 1 00
. 050
. 2OO
. 040
. 080
--(7.)
2.
1.
3.
1.
087
043
478
152
370
(CALC. UG/M3)
. 960+-
. 480+-
1.594+-
. 085 +-
. 607 +-
.047
. 02 1
. 174
. 03O
. 176
1
1
1
(RATIO)
. ooo-«
. 000+-
. 996+-
.216+-
. 963+-
. 115
. \ 13
. 165
.81f-
. 304
Fe
Cu
As
Sb
Pb
MEAS. AMB. MASS (UG/M3): 46.O
* - FITTING SOURCE OR ELEMENT
-------
Table 34
CMBDEQ RESULTS FOR CMB ** MB029
TOTAL SIZE FRACTION
SITE: PI-
SAMPLING DATE: 83122O SITE CODE: 5
SAMPLING DURATION: 24 HRS. WITH START HOUR: O
EFFECTIVE VARIANCE FITTING. REDUCED CHI SQUARE:
.299 D OF F:
(SOURCE)
4 SLBDMP *
7 RDBL.DG *
13 CDTTRL *
(UG/M3)
2.221-1 .500
1 . 270-!-- . 273
.629+- .101
(PERCENT)
6.346+- 1.461
3.630+- .799
1.796+- .300
TOTAL:
4.120+- .578 ' 11.771+- 1.748
(SPECIE)
1
2
~>
4
c:
Fe
Cu
As
Sb
Pb
*
*
X
*
#
--(MEAS. UG/M3)
.21
.21
*
.31
OH
OH
o+-
<
0+-
. 090
. O30
. 040
. 040
. 040
(7.) (CALC. UG/M3)
1 .829
. 600
. 600
.886
. 632+
.211+-
. 236+-
.012+-
.292+-
. 032
. O09
.019
.001
. 0 1 9
--- (RATIO)
.987+-
1 . OO3+-
1 . 122+-
. 000 +-
.941+-
. 1 48
. 15O
. 233
. 000
. 136
Fe
Cu
As
Sb
Pb
MEAS. AMB. MASS (UB/M3): 35.0
* - FITTING SOURCE OR ELEMENT
Table 35
CMBDEQ RESULTS FOR CMP # MB032
TOTAL SIZE FRACTION
SITE: P14
SAMPLING DATE: 83122O SITE CODE: 6
SAMPLING DURATION: 24 HRS. WITH START HOUR: O
EFFECTIVE VARIANCE FITTING. REDUCED CHI SQUARE:
.075 D OF F: 1
1
2
6
13
v ouuru-,t_ >
SLGSKM
BRKFLU
RDFOB
COTTRL
*
*
*
*
MJLJ/ n-:
2. 462+-
1 . 6884 -
13.683+-
2.485+-
-' /
.649
. 603
1 . 1 86
. 580
4. O35+-
2.767+-
22.431+-
4 . O74+-
lIM 1 >
1 . 083
.997
*-;. -* -« cr
.972
TOTAL:
20.317+- 1.590
33.307+- 3.O78
\ or c.uj.c. > vritHo. u
1 Fe
2 Cu
3 As
4 Sb
5 Pb
#
*
*
*
*
2.
2.
2.
.
1.
600+-
6004
8004
1 604
620+-
o/ no /
. 500
. 20O
. 3OO
. O30
. 1 50
\ /m f
4.262
4.262
4.590
.262
2.656
VL.HUU. UU>/
2.466+-
2.6204
2.8004-
. 160+-
1 . 620+-
1 PO /
. 123
. 1 37
. 126
.011
. 080
1
1
1
VI-vH \ 1U >
.949+-
. 0084-
. OOO+-
.999+-
. 000+-
. 188
. 094
. 116
. 199
. 105
Fe
Cu
As
Sb
Pb
MEAS. AMB. MASS (US/M3): 61.0
* - FITTING SOURCE OR ELEMENT
-------
Table 36
CMBDEQ RESULTS FOR CMS tt MB038
TOTAL SI7E FRACTION
SITE: P15
SAMPLING DATE: 831220 SITE CODE: 7
SAMPLING DURATION: 24 HRS. WITH START HOUR: 0
EFFECTIVE VARIANCE FITTING. REDUCED CHI SQUARE:
. 372 D OF F: 2
1
4
7
13
- i auursLit. >
SLGDMP
RDBLDG
CDTTRL
i. uu/ n -2>> i r tr.rvumv i t
*
*
*
2. 286+-
1 . 337 + -
. 595+--
. 5O1
.274
. 085
5. 7 15+-
3. 343+-
1.4BB+-
1 . 285
. 705
. 226
TOT AL:
4.21B+-
,77
1C.546+- 1.537
r i
1
2
T;
4
5
iL^ltl ^
Fe1 *
Cu *
As *
Sb *
Pb *
\. ntHs . uux no > -
. 660+-
. 220+-
. 2J.O+-
:'
. 290+-
. 090
. O3O
. 040
. 040
. 030
-I/.,1 IUMH-. U13/
1 . 650
.550
.525
.725
. 653+-
. 22O+-
. 230+-
.012+-
. 282+-
1 1 _> .'
. O33
. 0 1 0
.038
. 00 1
.01 8
vrsH i lu
. 990+-
1 . OO2+-
1 ,094+-
. 000+
. 972+-
. 144
. 343
.226
. ooo
. .! 1 5
Fe
Cu
As
Sh
Pb
MEAS. AMB. MASS : 40.0
* - FITTING SOURCE OR ELEMENT
-------
Table 37
List of Source Code Definitions
Code
0001
0002
0003
0004
0005
0006
0007
0008
0009
0010
0011
0012
0013
0014
SLGSKM
BRKFLU
HOOD
SLGDMP
MARTIN
RDFOB
RDBLDG
RD79BT
RRTRAK
FLUE
CALCIN
CUCONC
COTTRL
ASPLNT
Definition
Reverbatory Furnace Slag Skim (Fine)
Number 1 Brick Flue (Fine)
Number Converter Secondary Hood (Fine)
Slag Dump (Coarse)
Martin Mill Weighing Floor (Coarse)
Roadway Dust by Fine Ore Bins (Coarse)
Roadway Dust by Sample Bldg. (Coarse)
Road Dust 49th and Baltimore (Coarse)
Railroad Track South Gate (Coarse)
No. 1 Flue Dust (Coarse)
Herreshoff Roaster Calcine (Coarse)
Lepanto Copper Concentrate (Coarse)
S02 Cottrell Dust (Coarse)
Arsenic Plant Product (Coarse)
-------
Table 38
Maximum Source Contributions
Source
Herreschof f
Roaster
Herreschof f
Roaster
Calcine
Road Dust 49th
& Baltimore
Railroad Track
South Gate
Slag Dump
Composite
Slag Dump
Fine Total
Fine Ore Bin
Road Dust
Sample Bldg.
Road Dust
Martin Mill
Floor Dust
Copper
Concentrate
yg/m3
% As
yg/m3
% As
yg/m3
% As
yg/m3
% As
yg/m3
% As
yg/ra3
% As
yg/m3
% As
yg/m3
% As
yg/m3
% As
yg/m3
% As
181714
0.44
17.6
0.25
10.0
0.47
18.8
1.1
44.0
0.10
4.0
0.027
1.1
0.13
5.2
0.24
9.6
0.47
18.8
0.49
19.6
131322
0.74
30.8
0.42
17.5
0.74
30.8
1.8
75.0
0.17
7.1
0.042
1.8
0.21
8.8
0.39
16.3
0.80
33.3
0.83
34.6
181753
0.77
26.6
0.44
15.2
0.47
16.2
1.1
37.9
0.10
3.4
0.027
0.9
0.13
4.5
0.24
8.3
0.83
28.6
0.87
30.0
182256
0.91
32.5
0.52
18.6
1.0
35.7
2.4
85.7
0.23
8.2
0.058
2.1
0.29
10.4
0.53
18.9
0.99
35.4
1.0
35.7
50
-------
>1 iFinei 0
.' tCacnc)
Burr,
So.I (F.n.) V fispr
So,! iTeto;) T A»;:
Rcoc 3u&i IF:n*) ^ Roc1
Rooe DUJI i'oiol) A RjC'
Cool [Fin.I O
Cool
, F r.« i C
iTcic.-l
VC(i03ilH}
e
3
-
.5 is.-
I
< L
« 'Oh
"
a.
st-
D
'COMcUSTiON
tte . . . i .
EoMni Cruiici AxroQi S
1 * *
1
TI~ ,
H-A-. a^ __-_ i
I I -r ^ ""l""
1 ""'
GEOLOGICAL
...,!.,..,«, ' ...'.. ..,.
10 IS 20
Percent Silicon
Figure 1. Plot of the percent Al and Si for combustion and geological
sources. These sources would be difficult to resolve using
only these two elements (dimensions).
51
-------
6EOLOC1C-JL
Soil (Fin*) V
So! (Tote!) f
Rooc Dull iF.nc) &
Rood Dull IToiol) A
Cool (Fmi) O
Cool ICoOMt) *
Eortht Crystal Avtroo.
Ajpfn«i O
Roc). CruIMr CToicI)
Sourct VoiioDclitj
Figure 2. Three dimensional plot of the Fe, Al, and Si in geological
type samples. The addition of the Fe dimension effectively
improved the source resolving capability, i.e., the angle
between the coal fly ash and crustal average has increased.
Soil (F.'nt) V
Soil IToiol) ^
Doao Ou«t (Finil A
Boon Dull (Taiail A
AipnaM (Fint)
Atpnoii ITcioi)
Rod CulMt IF:n«]
Roci Crukh«r (Toloi
Cool (Coo't*)
Eorth« Crviiol Avucji
Figure 3. Three dimensional plot for the As, Al, and Si composition in
geological samples. The addition of As has greatly improved
the separation of the fine coal fly ash from the other
sources. Other coal fly ash samples have been reported to
contain even higher As concentrations.
52
-------
{2} Slag Dump
(7) 52nd & Bennett
j) 49th & Baltimore
*JA»CC 0*AW|NO 177*4. OAftD OICI^MI ), Iff?.
FIGURE 3.2-2
PHYSICAL LAYOUT Of TACOMA SMELTER
Figure 4. Physical layout of the ASARCO-Tacoma smelter showing the location of the bulk samples collected
for analysis.
-------
Slag Skim
As
Cu Cone.
Martin Mill FD
Herreschoff Roaster
'FOB FD
20
%Cu
' Secondary Hood
Figure 5. Vectorial representation of three elements from
selected source profiles.
%Pb
54
-------
% As
60J
50.
40-
30
Brick Flue
Slag Skim
Secondary Hood
It- 20
I- 10
Slag
10
20
T~
30
%Fe
40
Martin
15
Sample Bldg. RD
Cone.
reschoff Roaster
OB RD
Figure 6. Vectorial representation of three elements from
selected source profiles.
55
-------
All Sources Studied
Slag Four
Converter
Stack
Slag
Martin Hill Dust
Fine Ore Btn RD
Sample Bldg RD
As Baghouse
RD, 52 & Bennett
RD, 49 & Baltimore
RR Track Dust
Flue Deposits
Herreshoff Calcine
Cu Concentrate
Cottrell Dust
Herreshoff Charge
As Product
Fine
Slag Pour
Converter
Stack
Coarse
Slag
Martin Mill Dust
Fine Ore Bin RD
Sample Bldg RD
- As Baghouse
RD, 52 6 Bennett
RD, 49 & Baltimore
RR Track Dust
Flue Deposit
Herreshoff Calcine
Cottrell Dust
HerreshofC Charge
As Product
Cu Concentrate
High A3 to Cu. Sb & Pb
Slag Four
Lower As to Cu, Sb & Pb
Converter
Stack
High Al and Si
Herreshoff Charge
Herreshoff Calcine
RD, 52 & Bennett
RD, 49 & Baltimore
RR Track Dust
Slag
Fine Ore Bin RD
Sample Bldg RD
Martin Mill Dust
Low Al and Si
Cottrell Dust
Aa Product
As Baghouse
Flue Deposit
Tall Stack
Stack
Ground Level
Converter
High Fe to Cu, Aa. Sb. Pb ratio
Slag
Si » Cu, As, Pb
RD, 52 & Bennett
RD, 49 & Baltimore
Si s Fe » Cu » Pb
RR Track Dust
Si s Fe < Cu » As, Pb
Herrenhoff Charge
Herreshoff Calcine
Te Cu » As 3 Pb, Cu > Si
Fine Ore Bin RD
Sample Bldg RD
Martin Mill Dust
Fe a Cu > As » Sb, Pb
Cu Concentrate
As » Pb, Sb > Pb
As Product
As Baghouse
As » Pb. Pb > Sb
Flue Deposit
Pb > As, Low Cu
Cottrell Dust
Figure 7. Schematic Categorization of Sources Based on Chemistry and Particle Size
-------
DIRECT AND INDIRECT CONTRIBUTIONS
TO SUSPENDED PARTICULATE MASS
Figure 8. Illustration of direct and indirect smelter impacts on
air quality. (From Kellogg report, NEA).
SCHEMATIC DIAGRAM OF THE SOURCES AND SINKS
OF AEROSOLIZABLE DUST
AEROSOLIZABLE DUST LAYER
ACCUMULATION LAYER
Figure 9. Schematic diagram of the sources and sinks of aerosolizable dust
57
-------
SILVER KING SCHOOL
Percent Quarterly Lead
(Geometric Means)
lOCrq
10
P
E
R
C
E
N
T
Mean
8.76%
1.0-
0.3r-
.01
= 4.0 months
Mean
0.35%
12341
1978
iiiiiiiiiiiiiiii i i r
2341234123412341234
1979 1980 '1981 1982 1983
QUARTER
Figure 10.
Percent quarterly lead levels at Silver King School
Kellogg, Idaho
58
-------
MEDICAL CLINIC
Percent Quarterly Lead
(Geometric Means)
100
10
Mean
5.242
1.0
= 3.5 months
Mean
0.30%
0.1 _
.01
Tiiiiiiiii iii r
34123412341234
1980 -1981 1982 1983
QUARTER
~iiir-
1234
1978
1 2
1979
ir
3 4
~ir
1 2
Figure 11. Percent quarterly lead levels at a doctor's clinic
in Kellogg, Idaho.
59
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