AMBIENT AIR ANALYSIS OF BUNKER
HILL LEAD EMISSIONS
Prepared for:
The U.S. Environmental Protection Agency
Region X, 1200 6th Avenue
Seattle, WA 98101
Kenneth A. Lepic, Project Officer
Order #1Y0117NASX
Prepared by:
Ian H. von Lindern, P.E., Ph.D.
Route 3, Buhl, ID 83316
September 30, 1981
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LIST OF TABLES
Table Page
4.1 Point Source Inventory Information 14
4.2 Process Fugitive Sources 16
4.3 Point Source Category Components and Lead Emission Rates .... 17
4.4 Active Fugitive Source Characteristics 19
4.5 Passive Fugitive Source Characteristics 25
5.1 Regression Statistics for Selected Stepwise Model 40
5.2 Regression Statistics for Final Logarithmic Model 41
5.3 Parameter Values for the Logarithmic Model 43
5.4a Regression Statistics for Final Source Model 54
5.4b Regression Statistics for Final Source Model 55
5.5 Summary of Mean Quarterly Ambient Air Lead Impact Estimates, 57
Predicted Values, and Observed/Predicted Ratios
5.6 Source Categories' Critical Seasons and Impact Areas 66
5.7 Critical Quarters and Principal Sources for Non-Attainment
Monitors 53
6.1 Tabulation of Possible Causes for Extreme Air Quality
Excursions 80
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LIST OF FIGURES
Figure Page
3.1 Map of Study Area Showing Monitor Locations 7
5. la Comparison of Standard and Derived Horizontal Dispersion 43
Parameter Estimates
5.Ib Comparison of Standard and Derived Vertical Dispersion
Parameter Estimates 49
5.2 Percent Relative Impact Estimates for Ambient Lead by Source
Category at Each Monitoring Location 63
5.3 Predicted Ambient Lead Impact Estimates by Source Category
at Each Monitoring Location 54
6.1 Attainment Curve for No Improvement in the Passive
Source Category 73
6.2a-e Attainment Curves for Various Low-Level Source
Reduction Scenarios 75
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CONTENTS
Page
1.0 EXECUTIVE SUMMARY l
2.0 CONCLUSIONS 4
3.0 LEAD CONTAMINATION IN SHOSHONE COUNTY, IDAHO 6
4.0 SOURCE INDENTIFICATION AND EMISSIONS EVALUATION 10
4.1 GENERAL 1°
4.2 POINT SOURCES 13
4.3 PROCESS FUGITIVE SOURCES 15
4.4 ACTIVE FUGITIVE SOURCES 16
4.5 PASSIVE FUGITIVE SOURCES 22
5.0 AMBIENT ANALYSIS 27
5.1 GENERAL 27
5.2 THE MODELING PROCEDURES AND ASSUMfTTIONS 30
k
5.2.1 The Basic Source-Receptor Model 30
5.2.2 Variable Construction 31
5.2.3 Finding the Model Parameters 37
5.3 THE IMPORTANT SYSTEM VARIABLES 39
5.4 APPLYING THE BASIC SOURCE RECEPTOR RELATIONSHIP 52
5.4.1 Methodology 52
5.4.2 Model Results by Station 58
5.4.3 Model Results by Source Category 62
5.5 DISCUSSION OF MODELING RESULTS 65
6.0 STRATEGIES FOR ATTAINING THE MAAQS 69
6.1 ATTAINMENT CURVES 69
6.2 EXTREME EXCURSIONS 74
6.3 DISCUSSION 83
REFERENCES 85
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IV
Page
APPENDIX
A. REFERENCED MAP DISPLAYS 90
B. NATIONAL EMISSION DATA SYSTEM 102
C. SOURCES LISTED BY SOURCE TYPES AND SOURCE STRENGTHS 106
D. FINAL RELATIVE IMPACT ESTIMATIONS BY QUARTER AND STATION .114
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1.0 EXECUTIVE SUMMARY
This study utilizes computer-assisted cartographic modeling to
empirically relate ambient lead and cadmium particulate observations to known
emission estimates in the Silver Valley of northern Idaho. Through this
technique, the many factors influencing ambient lead concentrations were
simultaneously considered and quantified. Particular attention was paid to
the multiplicity and inconstancy of particlate sources, the confounding
/
effects of complex terrain on local meteorology, and the impact of windblown
dusts.
All of the known particulate sources in-the valley were included in one
of the following five categories, chemical consituency and magnitude estimates
were developed from existing reports or direct or inferred measurements: (1.)
Industrial Point Sources, (2.) Industrial Process Fugitive Sources, (3.)
Industrially-related Active Fugitive Sources, (4.) Transportation-related
Active Fugitive Sources, and (5.) Passive (windblown) Fugitive Sources. The
first three categories represent emissions from current industrial activities.
The latter two categories' emissions are residual in character and result from
cumulative effects over the years.
In the first portion of the analysis, cadmium measurements were used as a
tracer to quantify the important atmospheric dispersion characteristics
through the use of a Gaussian plume analogy. The results indicate that
mountain-valley drainage phenomena and associated nocturnal inversions
dominate dispersal activity on the majority of days. Under this situation,
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the standard Gifford-Pasquill dispersion parameter estimates hold for unstable
conditions. As neutral and stable conditions are encountered, dispersion
becomes more and more inhibited and Gifford-Pasquill parameter esimates are
grossly inadequate. This situation is often observed in complex terrain.
Stability is the critical variable in estimating pollutant dispersion in
the Silver Valley. However, there are several meteorological and operational
situations that require special treatment. They have to do with special
synoptic conditions, stable layers aloft, or smelter operations. Stagnant
high pressure areas, limited mixing depths, low wind speeds, and synoptic
drainage winds result in severe pollution episodes on about thirty to fifty
days per year. Inefficient smelter operations and frequent upsets and
malfunctions create severe episodes twenty to thirty days per year.
In the second portion of this study, the dispersion model was applied to
all lead sources and model estimates were related to observed ambient levels.
Two years of data were used. The result was a self-calibrated empirical
expression of lead impacts at monitored locations. Four source groups were
found to significantly impact ambient lead concentrations. They were:
Low-level smelter sources, or those that emanate from below forty feet, and
exhibit their greatest impact within one-half mile of the smelter. Most are
associated with the sintering operation or the ore preperation area. These
sources constitute over seventy-five percent of the lead impact in this zone
and absolute model estimates are as high as 6.5 ug/m3 on a quarterly basis.
Mid-level smelter sources or those that emanate from forty to one hundred
forty feet and exhibit their greatest impact at about two miles from the
smelter. Blast furnace upsets and pellet dryer emissions constitute the bulk
of this category. Their maximum modeled estimate was 3.5 ug/m3 at two miles.
-------
Active fugitive sources, generally road dust and sinter storage, and Passive
fugitive sources (windblown dusts) exert small but significant lead impact at
several stations. The former accounts for less than five percent of total
impact and the latter as much as twelve percent in summer months at
non-attainment"locations.
Current smelter emissions from the low and mid-level sources account for
at least eighty-five percent of total lead impact within eight miles of the
smelter. Tall stack emissions are insignificant in comparison. The highest
ambient concentrations occur in winter months and are associated with
prolonged periods of atmospheric stability. It is most important to remember
that the different sources' impacts vary with location and season and that
their combined effect determines the limiting situation for standard
attainment. Control of upsets and malfunctions is also critical to the
development of any implementation strategy. These items are discussed in
detail in the final section of the report.
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2.0 CONCLUSIONS
Meeting the NAAQS in the Silver Valley may be extremely difficult
for the following reasons:
a. The standard is small (less than 10%) when compared to
current and historical lead levels.
b. There are several types of sources capable of significantly
impacting the NAAQS concentration.
s
c. The maximum effects of these different sources occur at
different locations and during different seasons, consequently
al 1 must be treated.
d. Ambient concentrations are very sensitive to severe events,
both meteorological and operational; any control strategy must
be capable of accomodating serious upsets or prolonged
stagnations.
e. There are numerous residual pollutant effects that are not
well understood.
Source reductions will have to be accomplished in several source
categories and all to high degrees.
a. 80% to 90% reductions in all low and mid-level smelter point
and process fugitive sources.
b. A comprehensive program of controlling and/or eliminating
upset conditions, especially at the smelter blast furnace.
c. A program of reducing transportation related and sinter storage
related active fugitive emissions in and around the smelter.
d. A program of surface stabilization of contaminated soils in
the airport area, fairgrounds-lumberyard industrial corridor,
and Bunker Hill tailings pond embankment.
Evidence suggests that configuration changes, both in emissions
and real property, may be viable alternatives to emission reductions
in some locations.
a. As tall stack emissions do not have a significant impact in
comparison to low and mid-level sources, venting these sources
through the main baghouse would significantly reduce impacts.
This would be especially pertinent to blast furnace and pellet
dryer emissions.
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b. As low-level emissions reductions are almost wholly dependent
on their impact at Silver King and Smelterville, this require-
ment could be significantly reduced were the company to purchase
these properties.
4. An updated emissions inventory and a detailed analysis of OSHA
compliance activities are needed.
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3.0 LEAD CONTAMINATION IN SHOSHONE COUNTY, IDAHO
Attainment of the National Ambient Air Quality Standard (NAAQS) for lead
will requre substantial control and capture of current lead emissions in the
Silver Valley. There are numerous sources of lead to the ambient air in this
area and their impact varies considerably with location and season. In depth
analyses of the complex source-receptor relationships are necessary. Such
studies usually involve diffusion model applications. This area, however, is
particularly ill-suited for diffusion modeling.
The study area is an enlogated, narrow, deep valley encompassing the
South Fork of the Coeur d1 Alene River. Figure 3,1 is a map of the area.
Appendix A contains computerized representations of the Valley's industrial
and topogapghic features. The valley is subject to adverse meteorological
conditions and the regular formation of surface-based inversions and diabatic
winds. There are large denuded areas where deposition of water and windborne
contaminants have combined with industrial wastes to create large area sources
associated with transportation activities and inconstant industrial processes
combine with large point sources to effect a complex source spectrum with
considerable spatial and temporal variation. The smelting complex is the hub
of the industrial community. There are five principal processes within the
industrial area: (I.)-a galean (Pbs) ore mine, (2.) a lead/zinc milling and
concentrating operation, (3.) a lead smelter, (4.) an electrolytic zinc plant,
and (5.) a phosphate fertilizer production plant. Vegetation levels and soil
characteristics are important to this study. Denuded areas subject to
deposition of heavy metals could contribute to lead exposures through
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Monitor
Symbol Number
Cataldo CAT
Kingston KIN
Pinehurst PNH
Smelterville SflV
Silver Kinq School SKS
Kellogg Medical Center KMC
Kellogg City Hall KCH
Osburn
Wallace
1
10
2
3
4
5
6
7
8
"1 ^ «->2
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reen.trainment of the exposed soils. A variety of vegetation is found in
the area. A considerable change in the natural vegetation has resulted from
prevalent industrial activities. Much of the original coniferous forest was
harvested for timber and fuel. Sulfur dioxide gas from the smelter has
damaged or destroyed much of the native vegetation near Kellogg, and has
increased soil acidity to levels intolerable for many species. Following a
1910 fire and installation of the present smelter in World War I, natural
plant invasion did not occur and young trees did not survive. Most of the
burned area outside the smelter influence has been retimbered by second
growth lodgepole pine. Bushy plants and grasses cover the hillsides in
uneven distribution dependent on moisture availability (University of Idaho,
1974).
Soils in the area vary with their location. On the mountain slopes
parent materials consist of decomposed rocks and a thin layer of forest
litter. In scattered areas at lower elevations, small amounts of volcanic
ash and loess are found (University of Idaho, 1974). In an area of high rain-
fall and steep slopes devoid of protective vegetation, these materials erode
rapidly. Accelerated erosion destroyed many of the acid-resistant plants in
the smelter zone. The loss of surface soil and decreased water-holding
capacity increased runoff. The result is the bare, severely eroded hills
surrounding the smelter. Little or no vegetation is found there. The soils
are surface hardened remnants of the native materials.
The valley floor is partially filled with alluvial deposits varying
*
in thickness from a few inches to several feet. The alluvium is principally
unconsolidated sand and gravel. Mine and mill tailings have formed a metal-
laden veneer of silt over large areas of the valley. The use of mine wastes
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for fill and construction activities nave also resulted in redistribution of
soil metals throughout the area (State of Idaho, 1974a, 1975a, 1978b). In
Appendix B are representations of vegetation cover and 501'1 metal distribu-
tions in the valley.
The climatic conditions show a mean annual temperature of 47.4°F,
a mean number of days (138) above 32°F, with average frost dates of May 12
and September 27. Mean annual precipitation is 31 inches ranging from 9.4
inches in January to <0.1 inch in August. Most precipitation is in the form
of snow with snow cover prevalent from December to late February. Thunder
showers persist from mid-June through July with short rainy seasons in early
spring and late September (NOAA, 1976).
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10
4.0 SOURCE IDENTIFICATION AND EMISSIONS EVALUATION
4.1 GENERAL
There are several sources of lead to the atmosphere in the Silver
Valley. These sources vary in magnitude, frequency, chemical constituency,
and configuration. Some categorization scheme is necessary for purposes of
discussion and analysis. Developing reasonable source categories requires
certain knowledge of the different sources' behavior and the responsible
environmental and industrial processes. Lead/zinc ores have been mined in
several locations in the Silver Valley for nearly one hundred years. All
currently active mines are east of the smelting complex. Galena (PbS) is
the predominant lead ore. The ore is concentrated by wet-chemical milling
processes near the mines. Both lead and zinc concentrates are produced.
Concentrates are typically 60% metal in the sulfide form and are the con-
sistency of coffee grounds. They are transported to their-respective
smelters by rail in a wet form.
Direct air pollution from these activities is insignificant. The
milling operations are wet and the material is transported before it dries.
Concentrates spilled on the roadways constitute an active source of heavy
metal particulate after drying. Other residual aspects of the milling oper-
ations can have significant air quality effects. Large quantities of tail-
ings containing significant amounts ;of heavy metals are produced by the
milling operations. Currently these tailings are stored in large ponds.
The dikes of these ponds are usually made of dried tailings. These
metal-laden, low pH, fine sandy materials are not conducive to vegetation,
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11
and the dikes and abandoned ponds can be significant sources of reentrained
fugitive dusts.
In the first eighty years of mining, tailings were discharged directly
into the river or dumped at convenient locations. Periodically, the river
would flood and deposit the waste material on the valley flood plain. These
silts are also a source of particulate reentrainment. Tailing sands are
typically 1 to 4% lead by weight.
After arriving at the smelters, the concentrates are dried and mixed
with other concentrates and residual materials in preparation for pyro-
metallurgical treatment. Zinc concentrates are roasted at the zinc plant to
produce a powdered oxide of zinc. Lead concentrates are "sintered" to
produce a lead oxide compound, similar to furnace clinkers, called sinter
product. Both of these processes burn the sulfur from the sulfide concen-
trations as a fuel. This roasting of the ores produces the tremendous quan-
tities of sulfur dioxide associated with non-ferrous smelters. There are
several process-fugitive sources associated with the materials handling
aspects of these processes. Those before roasting are in association with the
ore-preparation and crushing plants in the lead smelter. Drying of the con-
centrate compounds is a significant point source of particulates. These are
all fine particulate emissions, about 30% lead by weight in the sulfide form.
Materials handling of zinc concentrates seems to be an insignificant partic-
ulate source. Roasting of the ores is a significant particulate source in
both smelters. Over two-thirds of the total zinc plant emissions result
^
from ore-roasting. In the smelter, over a quarter of the total lead emis-
sions are associated with the "Lurgi" sinter operations. Most of these are
point source emissions from scrubbers attached to the process. Others are
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12
fugitive emissions associated with materials handling of the highly abrasive
sinter product. Point source emissions are generally fine particulate. The
fugitive source emissions vary from very fine to very large settleable
particulate. Lead concentrations are 10% or less in the zinc smelter and
45 to 60% lead in the sinter operations emissions. Both are in the oxide
form.
After roasting, base metals are recovered from the metallic oxides.
In the zinc plant, the powdered zinc oxide is dissolved in sulfuric acid
and zinc is recovered electrolytically. In the lead smelter, the sinter
product is combined with coke and smelted in a blast furnace. Oxygen-
enriched air is "blasted" into the furnace and the coke is burned as a fuel.
The coke also supplies carbon as a reducing agent in an oxidation-reduction
reaction that results in molten metallic lead. This is a violent operation
that produces over one-half of the total lead emissions in the entire com-
plex. Much of it is emitted from the facility's main stack after the par-
ticulates are removed in the smelter's primary particulate control facility,
the main baghouse. A significant amount is also discharged directly to the
atmosphere as a result of upsets in blast furnace operation. Emissions from
this stage of the process are fine particulates and metal fume of about 60%
lead content in the oxide and pure metallic form.
Following the smelting processes, the base metals move to their
respective refineries where they are concentrated to 99.99% pure form.
Some fine particulate emissions are associated with refinery processes.
In this analysis the sources are first separated into the gross
regulatory categories,.point sources, and fugitive sources. Point sources
are defined here as controlled sources that emanate from stacks or equivalent
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13
devices. Fugitive sources are all those sources that are not classified as
point sources. Four sub-categories of fugitive sources, based' on the
source's suspension energy, have been developed for these analyses. They
are (1) process fugitive sources that are attendant to and derive their
suspension energy from industrial processes, (2) active fugitive sources
associated with gross materials handling in industrial areas, (3) active
emissions arising from transportation activities, and (4) passive fugitive
emissions that are reentrained by the wind. All of the sources in the valley
were located on computerized maps prepared especially for these analyses.
These maps are presented and discussed in Appendix A. References to
the appropriate maps are made here.
4.2 POINT SOURCES
All point sources in the valley are inventoried by the NEDS classi-
fication system designation number. Table 4.1 shows the source characteristic
information available for the 32 point sources identified. The variables in
that table are as follows:
NEDS--the National Emission Data System identification number
UNIT--identifies the associated industrial process unit (i.e., 1 crushing
plant, 5 sinter or Lurgi operation)
PBFR, CDFR--percent lead and cadmium, respectively, in the particulate
emission
EXITVEL—exit velocity of the stack emission (m/s)
STHGHT—physical stack height above ground (m)
STDIAM—physical stack diameter (m)
EXITTEMP--stack gas exit temperature (°K)
VOLFLOW—stack gas volumetric flow rate (m3/s)
YROPPCT--percen-t of annual operation time.
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Table 4.1 Point Source Inventory Information
DBS
1
2
3
4
5
6
7
8
9
10
11
12.
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
UNIT
1
1
1
1
2
3
5
5
5
5
5
7
7
8
B
9
11
20
21
21
22
23
23
23
24
25
25
25
26
31
32
33
NEDS
2
3
4
5
6
7
8
9
10
12
11
16
17
32
1
15
1
18
33
19
22
25
23
20
21
28
26
27
24
30
29
31
PBFR
31
31
31
31
31
31
45
45
45
45
45
5
5
55
10
10
10
20
10
10
20
10
5
5
5
5
0
0
0
CDFR
0
1
1
1
1
1
1
1
1
1
1
1
1
7
1
1
1
1
1
1
1
1
1
1
1
I
0
0
0
EXITVEL
5.20
10.50
10.90
10.50
10.90
10.90
14.30
9 .00
2.07
17.90
20.60
STHCHT
3.1
9.8
24.8
6.1
3.0
6.1
6.1
6.1
53.4
24.4
9.1
19.8
217.9
186.0
18.3
6.1
13.4
STDIAH
1.07
1.07
3.14
1.07
3.14
0.91
4.15
1.83
0.76
0.46
0.91
EXITTEMP
289
291
310
294
297
294
312
294
312
344
292
323
327
311
472
30
347
VOLFLOW
6.62
3.70
9.93
4.72
1.66
9.45
84.46
9.45
84.46
7.08
4.32
1926.00
193.10
23.62
0.94
1.32
14.17
YROPPCT
50
50
50
80
75
75
100
75
85
75
85
85
85
75
11
100
50
0
NAME
Crushpl Dryer
Crushpl Collect
Cruahpl Rodmlll
Crushpl Baghouae
Oreprep Baghouae
Pellet Dryer
Lurgl D Scrubber
Lurgl N Rotoclon
Lurgl B Scrubber
Lurgl A Scrubber
Lurgl C Scrubber
ZH Fume Main St.
ZN Fuming Gran
Pbreeln Scrubber
Reverb Baghouse
EF Gran Scrubber
Smelter Main St.
Zinc Main Stack
Concent Dryer
Concent Silo
Rosconv Scrubber
Meltdts Scrubber
Rodroas Baghouse
flWedge Scrubber
Residue Dryer
Scrap Furnace
*3Helt Scrubber
f2Melt Scrubber
ZH Pure Baghouse
AMP Reactor
AMP Dryer
Doyle Reactor
PS HUH
1
1
1
1
2
3
4
4
4
4
4
. 5
6
7
7
7
8
9
10
10
11
11
11
11
12
12
12
12
12
13
13
13
variables defined on page 26
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15
NAME--emission point name
PSNUM—point source identification number
With the exception of the last variable, these data were gathered from pre-
vious reports of PEDCo (1975), Valentine and Fisher (1975), PES (1978), and
EPA (1975a,c). The last variable, PSNUM, is a categorization variable
developed for the purposes of this study. It is explained in a later section
of the report. Point sources are located on the Map PTSOURCE in Appendix A.
4.3 PROCESS FUGITIVE SOURCES
These emissions are associatedliwith the metals industry. These are
pollutants that escape to the atmosphere from industrial processes. They
may be leaks, vents, and overflows from production and pollution control
equipment or buildings. They may escape from uncontrolled portions of pro-
cesses exposed to the atmosphere, such as conveyor belts or by-product
dumps. Many of these fugitive sources are attendant to the processes and
exhibit regularity in their location and strength (e.g., building fans).
Others, particularly leaks and overflows associated with process upset
conditions and malfunctions, are erratic in both frequency and magnitude.
Process fugitive particulate sources were identified and mean
emission rates were estimated in previous studies (PEDCo, 1975c; Valentine
and Fisher, 1975; PES, 1978; PEDCo, 1979). Those sources have been identi-
fied in Table4.2, together with percentage lead and cadmium components
measured in the same surveys.
«•
All industrial process-related lead sources were combined in cate-
gories for later analyses. Those sources and mean emission rate estimates
and rankings can be found in Table 4.3. This table represents the best
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16
Table 4.2 Process Fugitive Sources Ordered by Lead Source Strength
Name
Blast furnace
CPP exhaust fams
CRE con exhaust fans
Cast roof fans
PB ref roof vent
Elec fur roof
Sinter prod dump
EMR
30.00
34.00
25.00
12.40
6.70
4.30
0.54
PBFR
61
31
34
31
37
10
31
CDFR
9
1
1
1
1
0
1
PBEMR
18.3000
10.5400
8.5000
3.8440
2.4790
0.4300
0.1674
CDEMR
2.7000
0.3400
0.2500
0.1240
0.0670
0.0000
0.0054
EMR—total particulate emission rate, Ib/hr; PBFR—percentage lead
in emission; CDFR—percentage cadmium in emission; PBEMR—lead emission
rate, Ib/hr; CDEMR—cadmium emission rate, Ib/hr.
estimates for comparing industrial sources that could be developed with the
available data. It was developed from information collected between late
1974 and early 1979 and some updating may be required.
4.4 ACTIVE FUGITIVE SOURCES
These sources are varying and intermittent pollutant sources whose
suspension energy is provided by agents other than steady state industrial
processes. They may be industrial sources related to activities such as
stockpiling," truck and train loading and unloading, or materials handling.
They may be related to land use such as ground working, construction, or
surface mining. Or they may be related-to transportation sources such as
reentrainment by vehicular traffic, from open carriers, or mobile source
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17
Table.4.3 Point Source Category Components and Lead
Emission Rates (EMR)
I. Smelter Low-Level Sources
NEDS
2
3
4
5
6
8
9
10
11
12
17
32
15
FUG
FUG
FUG
FUG
FUG
FUG
Name
-
Crushing Plant Dryer
Crushing Plant Collector
Crushing Plant Rodmill
Crushing Plant Baghouse
Oreprep Baghouse
Lurgi D Scrubber
Lurgi N Rotoclon
Lurgi B Scrubber
Lurgi A Scrubber
Lurgi C Scrubber
Zinc Fume Granulator
PB Refinery Scrubber
Electric Furnace Scrubber
Oreprep Exhaust Fans
Ore Cone Exhaust Fans
PB Refinery Roof Vent
BF Roof Vents
Electric Furnace Roof
Sinter Product Dump
Total Low Smelter PB EMR
Mean Lead-
EMR Ib./hr.
3.4
.4
.4
.4
.3
1.4
16.0
4.1
3.4
2. 7
1.8
10.5
8.5
2.5
.9
.4
._2_
57.3
% of
Category
6
1
1
1
1
2
28
7
6
5
3
0
0
18
15
4
2
1
1
EMR
Rank
6
12
13
14
16
10
1
4
5
7
9
--
--
2
3
8
11
15
17
II. Smelter Mid-Level Sources
NEDS
7
16
FUG
FUG
FUG
Name
Pellet Dryer
Zinc Fume Main Stack
Blast Furnace
Casting Roof Fans
Fuming Furnace Roof
Mean Lead
EMR Ib./hr.
8.0
2.3
18. 3
3.8
1.3
% of
Category
24
7
54
11
4
EMR
Rank
2
4
1
3
5
Total Mid Smelter PB EMR
33. 7
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18
Table 4.3 Continued
III. Smelter High Level Sources
NEDS
1
Name
.
Smelter Main Stack
Mean Lead
EMR Ib. /hr.-
43.0
% of
Category
100
EMR
Rank
1
IV. Zinc Plant Low-Level Sources
NEDS
33
19
22
25
23
20
21
28
26
27
24
Name
Concentrate Dryer
Concentrate Silo
Rosconv Scrubber
Melt DRS Scrubber
Rodross Baghouse
//I Wedge Scrubber
Residue Dryer
Scrap Furnace
//3 Melt.
//2 Melt.
ZN Pure. Baghouse
Total ZN Plant Low Level
Mean Lead
EMR Ib. /hr.
.02
0
0
.07
.02
1.36
0
.07
.37
.11
0
2 .02
% of
Category
1
0
0
3
1
67
1
3
18
5
0
EMR
Rank
5
--
--
4
6
1
7
4
2
3
—
V. Zinc Plant High Level Sources
NEDS
18
NEDS
31
32
33
Name
Zinc Plant Main Stack
VI. Ammonium-Phosphate
Name
AMP Reactor
AMP Dryer
Doyle Reactor
Mean Lead
EMR Ib./hr.
2.28
% of
Category
100
EMR
Rank
1
Plant Sources
Mean Lead
EMR Ib./hr.
0
0
0
% of
Category
EMR
Rank
--
—
-------
19
combustion emissions. They are distinguished from the previous category in
that their frequency and magnitude are not attendant to industrial processes
in the steady state time frame, and from the following category in that
their suspension energy is independent of kinetic meteorological factors.
Active Sources (Industrial) —Particulate fugitive sources were identified
in the same surveys cited in the process fugitive discussion. Similarly,
mean emission rates and chemical constituencies were estimated in those
studies. These sources are identified in Table 4.4. They can be located
on Map ACTIVES in Appendix A.
Table 4.4 Active Fugitive Source Characteristics
Name
Silica slag pile
Sinter storage
Slag storage
State highway pil
Sinter -storage
Coke storage
Map ID
73
45
33
es 74
49
45
EMR
17.8
1.6
20.2
30.3
0.4
0.6
PBFR
5
42
2
1
42
0
CDFR
0
1
0
0
1
0
PBEMR
0.890
0.672
0.404
0.303
0.168
0.000
CDEMR
0.000
0.016
0.000
0.000
0.004
0.000
EMR—total particulate emission rate, Ib/hr; PBFR—percentage lead
in emission; CDFR--percentage cadmium in emission; PBEMR—lead emission
rate, Ib/hr; CDEMR—cadmium emission rate, Ib/hr.
-------
20
Active Sources (Transportation)—Several researchers have investigated
those factors most contributory to road dust impacts. The condition of the
road surface seems most important. Unpaved roads result in orders of magni-
tude greater particle suspension than paved roads. In fact, some studies
"have ignored the effect of particle resuspension from paved roads alto-
gether (PES, 1978). However, others have found.considerable resuspension
of tracer materials from paved roads (Sehmel, 1973). The PEDCo (1975a)
study recognized three categories of roads: unpaved, paved, and dusty paved.
In general, it seems that in comparison to unpaved roads, paved road contri-
butions to total suspended particulate may be negligible. However, with
respect to hazardous materials or trace elements deposited on roadways,
traffic induced resuspension may be significant (Sehmel, 1973). Suspension
from unpaved roads has been related to particle size in the roadbed (Becker
and Takle, 1979). PEDCo (1975a) has developed formulas for total particulate
suspension from unpaved roadways based on traffic characteristics and the
silt content of the roadbed.
Various studies identify differing ambient impacts associated with
vehicle weight and speed and the number of vehicle passes (Becker and Takle,
1979; PEDCo, 1976; Sehmel, 1973; Smith, 1976). Vehicle characteristics
affect both suspension rates and initial dilution through mechanically
induced turbulence. The latter results in a shallower dilution gradient
away from highways and a lesser dependence on atmospheric stability and
source height (Becker and Takle, 1979; Sistla et al., 1979).
*
Other factors found to be important in road dust impacts are the
angle between the wind and road (Calder, 1973; Sistla et al., 1979) and the
surface roughness of the roadside environment. Little and Wiffen (1979),
-------
21
Smith (1971), and Heichel and Hankin (1976) found the characteristics of
roadside flora significantly affected ambient lead concentrations. Becker
and Takle (1979) suggest that sampling height is an important consideration
due to the differing particle sizes and settling rates of the suspended
material.
Each of the roads identified in this study was characterized as
paved, unpaved, or dusty paved. Annual vehicle miles traveled (VMT) for each
road were obtained from previous reports and traffic studies (PEDCo, 1975c;
PES, 1978; State of Idaho, 1974a). Emission Rate Factors developed by the
EPA as described by PEDCo (1975c) were then applied to get per unit distance
emission rates for these roads. Both road dust and gasoline combustion
factors were included.
Active Sources (Urban)—Urban active fugitive source strengths were con-
sidered to be proportional to the traffic volume on city streets. The total
VMT estimates for each city were obtained and proportionately allocated
over the area of the community. No absolute estimates of emission rates
were determined for the last two sub-categories as they were treated pro-
portionately in later regression analysis correlating these estimates to
observed concentrations. The details may be found in the parent document.
However, these sources are located on the Maps TOWNS and ROADS in Appendix A.
Specific emissions estimates could be developed for these sources by using
the final regression coefficients in a calibration procedure. However, this
would require additional work.
-------
22
4.5 PASSIVE FUGITIVE SOURCES
These sources are reentrained by the wind. They include open areas
of bare soil and exposed industrial areas. Their magnitude depends on wind
speed and direction and surface conditions. The air quality aspects of
these sources have attracted significant attention in recent years due to
the need for predicting urban air pollution levels, and in the nuclear
industry where resuspension of spilled nuclear materials is a possible
health hazard. Like roadway sources, passive source strengths are greatly
dependent on surface conditions. Soil particle size distribution, surface
roughness, orientation, cover, and moisture conditions are the principal
surface variables. Those factors that contribute most to wind suspension
have been investigated for many years in relation to soil erosion. Those
studies have .been modified to develop air pollution impact estimates (PEDCo,
1973; Wilson, 1975).
The basic technique utilized in assessing passive source impacts
was developed in a series of publications by Chepil and associates and was
reviewed by Woodruff and Siddoway (1965). In that publication, they
modified the techniques to develop "A Wind Erosion Equation." Those defini-
tions and techniques have been extended for use as air quality predictors
commonly termed the "Modified Wind Erosion Equation Method." The Modified
Wind Erosion Equation is as follows:
E = I r K1 • C1 • L' • V
where
E = total mass soil movement, tons/acre/yr
I = soil credibility (or the potential to erode), tons/yr
-------
23
K' = soil ridge roughness factor
C1 = climatic factor (soil moisture and wind velocity)
L1 = field length factor
V = vegetation cover factor
The strategy of this equation is that the credibility, I, is defined
as the potential loss of soil in tons/acre per annum from a wide, unsheltered,
isolated field with a bare, smooth surface. It is based on both wind tunnel
and field observations (Woodruff and Siddoway, 1965). The others are
mitigative variables varying in value from 0 to 1.0 that reduce the final
erosion estimate.
This equation has been used to estimate emission rates from area
sources by multiplying E by the area of the source in acres times a conver-
sion factor. That conversion factor represents that fraction of the total
soil movement (E) that is observed as ambient particulate. Literature values
for the conversion factor are found from 0.3 to 10% (Wilson, 1975).
The physics of erosion involves three transport processes:
saltation, surface creep, and suspension. Saltation is the movement of
soil by a short series of bounces usually rising no more than a foot above
the ground. Surface creep is the rolling or sliding of the larger-particles
along the surface. Suspension is the entrainment of fine particles up away
from the surface (Buckman and Brady, 1972). A significant gradient of
particle size with height results from these processes. Only the suspension
process produces long range air pollutants.
Erosion rates are also sensitive to meteorological variables.
Total soil movement varies with the wind speed cubed and inversely with
soil moisture (Woodruff and Siddoway, 1965). Rain and snow cover eliminate
-------
24
wind erosion when in sufficient quantity. In this area, diffusion of sus-
pended materials by wind erosion is practically independent of atmospheric
stability. Significant wind speeds are required to initiate suspension.
In the Silver Valley such wind speeds are observed only in near neutral
conditions and only in the up or down valley direction (PES, 1978). It
seems that significant reentrainment from these sources occurs in the Silver
Valley under meteorologically limited circumstances.
All of the available data for passive sources have been accumulated
from three studies conducted in the Silver Valley (PEDCo, 1973, PES, 1978;
State of Idaho, 1978). The sources can be found in Table 4.5 and located
on Maps found in Appendix A, by using the value VAL as described in Part E
of that Appendix. No absolute emission rate estimates were prepared for
these sources because of the nature of the later analyses as detailed in the
parent document. However, they are listed in their relative order of lead
source strength in Appendix C. Estimates could be accomplished by a calibra-
tion procedure utilizing the final regression coefficients. Such an analysis
would be interesting but is beyond the resources of the report.
-------
Table 4.5 Passive Fugitive Source Characteristics
10
to
?i
CJ
I
i
5
1
•re the cod* number! frcn the original Itudlei
referenced In the text.
VAl -- Is a number used to key the map Input information.
ERODE. ROUGH. LENGTH. VEG -- are the source development factors
for the Windblown Dust Equation as defined by Chepln.
PBPPH. CDPPH -- are the 'lead and cadmium ppra toll levels
PBFMC, CDFRAC -. are the ppn lead and cadmium levels In the
fines fraction of the soils samples where available.
CHARHOTE. PBHOTE, CDNOTE — The note denotes the source of the
data for the variables above. The value E refers to the
PES report. P to the Pedco reports. V to estimates by
author, H to Health and Welfare Department reports.
S t 1 to estimates fro* sivllar samples.
Til ft Of* COLCH
.M
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ro
en
-------
Table 4.5 continued
CROW IOICH
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. Hl
-------
27
5.0 AMBIENT ANALYSIS
5.1 GENERAL
The heavy-metal contamination problem in the Silver Valley is extremely
complex. The air pollution component is especially difficult. Considerable
care must be exercised in any analysis of the support data and the
particulars of the analytical procedures. In any type of modeling study where
mathematical expressions are used to represent physical phenomena, certain
assumptions have to be made to accommodate the analysis. The adequacy of
those assumptions most often determines the quality of the results and
conclusions. The types of assumptions that are inherent to simulation
diffusion models (at the practiced state-of-art) could possibly make such
analyses unreliable for the Silver Valley situation. However, given the
wealth of data available, an empirical application in this situation could
result in a more reliable analysis. Literature citations and a support
argument for this position can be found in von Lindern (1980b).
This is not to say, however, that there are no problems in an empirical
analysis. Analyses where observed pollutant concentrations are related to
atmospheric and emissions indices, in the ignorance of the physical
phenomena involved, are particularly prone to erroneous conclusions. Great
care must be exercised in the design of the model and the interpretation of
results. In any .modeling analysis, it is important to understand the
physical and anthropogenic factors involved. This background information is
-------
28
necessary to select and to evaluate the assumptions discussed above. Most
of this background material has been summarized in earlier sections. How-
ever, there are three areas of specific difficulty that should be discussed.
The problems are presented in detail in von Lindern (1980b). However, they
are important enough to repeat briefly in this presentation. They are:
1. Meteorological factors associated with complex terrain.
2. Spatial and temporal valuation in source strength and multiple
source configurations.
3. Interdependency of source strength and configuration with
meteorological variables.
Two atmospheric phenomena common to complex terrain literature are
especially important in the Silver Valley: the frequent formation of surface
based nocturnal inversions and the mountain-valley drainage wind. Radiative
cooling of the slopes of the valley causes air temperature to decrease near
the surface. As it cools, it becomes more dense and flows downs lope to the
valley floor and subsequently down valley. Extreme diurnal shear and low
level isothermal structures can result from this drainage. This capping
phenomena inhibits pollutant diffusion and enhances terrain channelling.
After sunrise the slopes of the valley heat more rapidly than the floor.
This results in the descent of the inversion layer as insolation proceeds
and fumigation phenomena can return residual pollutants trapped aloft over-
night to the ground. Following this period flow up the valley predominates.
This flow is typically associated with the prevailing synoptic winds aug-
mented by upslope winds resulting from differential heating. The valley
.*
narrows and deepens considerably in this direction. As a result, signifi-
cant terrain channel ling-is expected with up-valley winds, even in the
absence of stable layers aloft.
-------
29
Conventional modeling analysis dealing with long-term effects
requires some degree of uniformity and homogeneity in both atmospheric and
source behavior. The second difficulty in analyzing the Silver Valley via
modeling concerns the spatial and temporal irregularities in source behavior.
Source descriptions were provided in the last section. Point sources are
generally uniform in their behavior. Industrial processes are designed to
operate at particular rates and capacities. Control equipment is corres-
pondingly designed and emission rates are consistent. When dealing with
process fugitive sources, emission rates become irregular, depending on
process fluctuations and outside stimuli. These two categories are also
subject to upsets and malfunctions that can result in orders of magnitude
changes in emission rates for short periods of time. Active fugitive
sources are sporadic and their emission rates depend on the frequency of
their parent mechanical activity and local meteorological conditions.
Passive fugitive source emission rates are wholly dependent on meteorologi-
cal factors and vary not only in magnitude and frequency but in configura-
tion as well. Wind speed, wind direction, and surface conditions dictate
these sources' contribution to atmospheric levels.
This last factor, the interdependency of source strength and con-
figuration with meteorological variables is, perhaps, the most confounding
factor in applying modeling techniques to this problem. Ironically, it is
this same dependence that, all owed solution of this problem through the tech-
niques employed. As an excellent meteorological data base was available
through the Bunker Hill Company's Supplementary Control System (SCS) monitoring
network, source behavior could be quantified through meteorological indices.
However, doing so in an empirical format utilizing over thirty variables
-------
30
and two years of data from hundreds of sources was a tremendous computational
demand. In this study, the problem was addressed by using the geographic
information system described in Von Lindern (1980b).
5.2 THE MODELING PROCEDURES AND ASSUMPTIONS
5.2.1 The Basic Source-Receptor Model. The objective of this modeling
analysis is to quantify those factors most important in air lead contamina-
tion in the Silver Valley and to ascertain the relative contribution of
different sources to excess atmospheric concentrations. Considering the
wealth of data available and the particular difficulties of applying standard
diffusion models, an empirical approach was selected. The first step in the
solution strategy was to develop the basic source-receptor model describing
the fundamental relationship between a receptor and any source of exposure.
The design of that model should account for those particular topographical
emissions, and meteorological phenomena suspected of confounding air pollu-
tion impact analysis in this area. The most important of these considera-
tions have just been discussed. Cursory examination of the wind spectra in
the Silver Valley suggests that the mountain-valley drainage/nocturnal
inversion scenario dominates the local meteorology on the majority of days.
The model developed sought to quantify the basic source-receptor relationship
in a quasi-Gaussian fashion by accounting for the peculiar flows associated
with this diurnal phenomenon. It is also known that certain meteorological
conditions can void" or considerably modify the basic relationship. The Bunker
Hill Company SCS has identified those critical meteorological situations.
Their data are used to modify the basic relationship where appropriate by
dummy variable additions to the .regression procedure.
-------
31
5.2.2. Variable Construction. A derivation of the modeling equations and
construction of the variables and regression analyses are not contained in
this report but can be found in von Lindern (1980b). It is most important
in this report to point out and discuss the assumptions inherent to the mod-
eling strategy and how those assumptions affect the quality of the results
and limit the conclusions that may be drawn from these techniques.
The basic modeling equation was the simple Gaussian plume model
* •
where
x = the pollutant concentration at the receptor
Q = the initial pollutant source strength
u = the wind speed in the x direction
.ay and az = the standard deviations in pollutant concentration
in the y and z directions
x,y,z = the Cartesian coordinates
H = the difference in elevation between source and receptor
No provision is made for differential plume rise or particulate settlinq.
This model was derived in a surrogate form to accommodate those variables
that were available from the emissions, meteorological, and geographic data
bases. Three basic requirements were involved in the surrogate derivations:
(1) the equation had to be derived using variables that were both available
from the emissions and meteorological inventories and that were amenable to
-------
32
manipulation in the geographic information system, (2) the model had to be
capable of empirical parameterization via linear -regression, and (3) the
model, to the maximum extent possible, should reflect the concepts of
accepted air pollution meteorology. Simultaneously accommodating these
constraints involved making several assumptions and adjustments to the
solution strategy. Those basic assumptions are discussed below.
OBSERVATION TIME PERIOD BASIS. The dependent variable in the equation is the
mean 24-hour pollutant concentration observed at the various stations. These
data were obtained from the State's hi-vol network shown on page 20.
These data are believed to be of excellent quality and were used as is.
However, this daily periodicity defines the base time unit for the dependent
variables.
ADEQUACY OF EMISSION DATA. This represents what could be called the weakest data
base in the procedure. Only single observations were available for many of
the sources. Quarterly, self-reported, emission rates were available for
some smelter sources, but the reliability of these data is unknown. These
estimates were reduced to 24-hour emission rates. It is suspected that many
of these sources can vary more than an order of magnitude on a daily basis,
especially when malfunctions and upsets are considered. Two elements of
strategy were developed to deal with this deficiency. The first mitigative
factor was to introduce on-off criteria as described in the parent document.
Many sources were known to be inoperative on particular days for either
operations or meteorological reasons. Zero emission rates were assigned to
the appropriate sources for these days. The second mitigative element
-------
concerns the basic philosophy of the entire project. The source contribu-
tions and rollbacks eventually developed are discussed in relative rather
than absolute terms. Pseudo-dummy variable analysis, as described by
Draper and Smith, is utilized throughout. This technique is especially
useful in determining the relative significance of different variables in
regression analysis.
Source estimates were eventually grouped into eight categories for
final analysis. The grouping was based on the spatial configuration of the
sources. The basic assumption isifchat significant particulate lead sources
emanate from eight predominant locations in the valley. By appropriately
characterizing the emissions (by rate estimates and on-off criteria) and
the atmosphere (by wind and stability indices), relative contributions could
be ascertained by regression analysis.
THE VIEWFIELD ASSUMPTION. Downwind concentration dilution in the traditional
Gaussian formula is inversely proportional to the wind speed. Doubling the
wind speed doubles the space between particles and halves observed concen-
trations. However, this assumes a constant wind direction and speed. In
the Silver Valley wind direction and speed change continuously and usually
reverse themselves daily in association with the mountain-valley drainage
phenomenon. In order to compensate for this difficulty in the daily time
basis, two wind flow directions were assumed in conjunction with the mountain-
valley drainage phenomenon. Up-valley and down-valley winds were defined.
The mean daily wind speed, direction, duration, and standard deviation in
wind direction were calculated for each flow. Each receptor's face was
turned into the wind and its uostream "view" was considered. This is the
-------
34
VIEWFIELD assumption. How many sources a receptor can see depends on its
"viewfield" or a narrow upwind sector that contains those sources that could
possibly impact that receptor. The depth of view depends on wind speed and
duration of the wind flow from that direction. The width of the sector
depends on the variation in wind direction. An expression was derived to
express the number of hours per day that a receptor was exposed to a source
and the assumption was made that the downwind component of dilution was pro-
portional to that duration of exposure and inversely proportional to wind
speed and the width of the view-sector. These terms are all calculable in
the geographic information system and could be easily accomplished for each
receptor location. In this way, both a proportioning factor and an addi-
tional on-off criteria were developed. If, because of wind speed or direc-
tion, a receptor is not exposed to a source during the day the exposure
reduces to zero. If the wind blows consistently all day from source to
receptor the downwind dilution becomes the 1/u term in the traditional
equation. This variable is not meant to simulate wind flow in the valley.
It is an index meant to represent downwind dilution from sources in the up-
and down-valley directions and is designed for use in a regression equation.
THE STABILITY ASSUMPTION. The surrogate term for the —]— term in the
Y z
Gaussian model equation was designed so that the Gifford-Pasquill form of
these variables could be recovered from the eventual regression coefficients.
First the traditional form of the Gifford-Pasquill plots were derived in a
^
system of linear equations developed through logarithmic transformations.
It was then assumed that the linear coefficients for this system were a
function of atmospheric stability and would be specific for this situation.
-------
35
Next the potential temperature gradient at Spokane International Airport
was selected as the daily indicator of atmospheric stability. This has been
found, through the Company's SCS, to be the most appropriate stability index
available. It was then assumed that the linear coefficients were first
degree functions of the potential temperature gradient. This assumption
is consistent with the form of the Gifford-Pasquill charts and results in a
convenient form of the expression for both parameterization and geographic
manipulation.
2
THE PLUME MEANDER ASSUMPTION. The term expf-^-s-) in the Gaussian plume
analogy represents the dilution effect of being remote from the mean wind
vector. In the Gaussian formulation, a uniform wind direction is assumed
and the degree of lateral dilution is a function of atmospheric stability.
In the Silver Valley, wind direction fluctuates continuously and straight
line flow is not expected. Thus, it was assumed that exposure reductions
associated with being remote from the mean wind vector are more a function
of variation in the mean wind vector than of the stability criteria per se.
Two lines of reasoning justify this assumption. The variation in the mean
wind vector (plume meander) is likely a function of stability itself. And
the maintenance of a crosswind Gaussian distribution in the Silver Valley is
unlikely. Plume meander, when considered on a daily basis, would predom-
inate any such distribution. As a result the standard deviation in wind
direction was selected as a measure of crosswind remoteness and dilution was
assumed to be a function of the number of standard deviations a receptor was
from the mean wind vector.
-------
36
THE VERTICAL DISTRIBUTION ASSUMPTION. Differential plume rise, participate
settling, and plume depletion are all ignored in this model. A constant
plume rise and constant plume elevation are assumed. The main reason for
ignoring differential plume rise was the insufficiency of the data base for
developing appropriate variables. In order to account for plume rise, an
equation utilizing the atmospheric surface temperature and wind velocity
would have to be included in the source height factor. These measurements
are used in other independent variables in the modeling equation. Including
them in another term would increase the possibility of undesirable effects
associated with inter-variable correlation. Moreover, from a practical point
of view, these are low temperature plumes and only a single morning surface
temperature was available. The benefit of using that single observation in
predicting an effective daily plume rise was considered not worth the con-
founding effects of adding a possibly redundant variable. Considering plume
depletion, no data were available for developing settling estimates. Making
unnecessary assumptions in the development of regression variables should be
avoided. Practically, plume depletion must be significant with respect to
certain sources. Fugitive sources in particular contain large particle
emissions subject to considerable fallout. Point sources, on the other
hand, are fine particle emissions that may approximate gaseous behavior. As
explained later, cadmium, emissions are used to parameterize the basic source
receptor model. Cadmium emissions are predominantly fine particulate. Ig-
noring settling phenomena is, likely, permissible in defining parameters for
the model. Later, when developing source estimates for lead and particulates,
depletion from fugitive sources must be considered important. However, in
the division of source categories, active and passive sources appear in
-------
37
separate independent variables. When linear regression analyses are applied
to those variables it can be inherently assumed that some constant amount of
plume depletion is accounted for in assigning the regression coefficients.
In practical terms, the assumption is that a particular percentage of fugi-
tive emissions fall out between the source and nearest receptor and that
gaseous behavior is observed beyond. This assumption is adequate for the
empirical format of the study.
THE EMPIRICAL MODIFIERS. The Bunker Hill Company meteorologists operating
the Supplementary Control System (SCS) have long recognized those difficult
meteorological factors discussed earlier in this section. They have,
through years of experience, developed semi-quantitative indices to represent
the onset of certain meteorological and operational conditions. For the most
part, these variables identify the onset of non-routine conditions where
"normal" assumptions do not apply. As such they are quite appropriate for
use in regression analysis as dummy variables to account for the effects of
special conditions where the base model may not apply. These variables were
transformed to act as empirical modifiers in the regression analyses. De-
tails of the variables and transformations can be found in the parent docu-
ment.
5.2.3 Finding the Model Parameters. The model was assigned parameters
through a stepwise regression procedure that forced inclusion of the vari-
ables developed as the initial surrogate model. The several empirical
modifiers developed from the SCS were also offered for forward selection.
There are several assumptions inherent to regression analysis that are not
-------
39
necessary to include. However, there are two assumptions made in solving the
regression equation that are important to discuss here.
The first-is the use of cadmium data to find the model parameters. The
number of sources and the difficulties with defining configurations make it
impossible to solve the modeling equation for lead or total particulates.
Cadmuim emisions, however, seem to predominatly arise from within the lead
smelter or, more to the point, from the same geographic location. This
considerably reduces the complexity of the modeling equation for cadmuim.
Because cadmuim emissions do emanate from three distinct source heights, one
unknown function still precludes solution of the equation. An assumptive
constraint has to be added to the regression matrix. That constraint was
developed by assuming that the ratio between Oy and c^ at neutral stabilities
at 0.1 miles from the source will be the same as the ratio found under there
conditions in the traditional Gifford-Pasquill charts.
5.3 THE IMPORTANT SYSTEM VARIABLES
Using these two assumptions, the equation was solved and the selected
model is shown below. An extensive discussion of the results is found in von
Lindern(1980b).
The regression statistics for this model indicate that pollutant
dispersion can be successfully quantified by this model form. Seventy-three
percent of the- variablity in observed concentrations is explained at strong
significance levels. The initial model seems particularly strong (R2 =
.71 at p .0001). This is especially encouraging considering the
difficulties with cadmuim source estimates discussed earlier.
-------
Table 5.1 Regression Statistics for Selected Stepwise Model
•
Initial Model: DEPZ - BO + B2LNVWF + B-POTEMP + B.lnX + B5POTEHlnX + BgNSDSQ
Step
0
1
2
3
4
5
Source
INTERCEPT
LHVWF
POTEHP
LNX
POTEMP*LHX
NSDSQ
MD50LNX
RECVAR
BFDOWN
HD36
VIS
SS (Model)
SS (Error)
SS (Total)
Variable
Added
(Initial Model)
MDSOLMX
RECVAR
BFDOWN
HD36
W15
Total
Pi
Paraneter
Eatlaate
2.31
.17
.65
1.51
.15
-.04
.16
.20
.75
2 .06
.084
Model Statlatlci
SUB of 1
Square* Model
7749.4
7836.9
7870.1
7892.5
7914.2
7930.8
SS • 10877.5
irameter Statlatlca
SUB of
Squares
(SAS Type II)
31.0
1511.5
1999.4
898.2
91.8
33.0
33.2
40.9
25.4
16.5
7930.8
2446.7
10877.5
I by Step
F-Model (P>F) F-Varlable
on Entry (P>F)
673. 3(. 0001) (all .0001)
583. 3(. 0001) 39. 0(. 0001)
507. 3(. 0001) 15. 0(. 0001)
448. 2(. 0001) 10. 2(. 0015)
402. 1(. 0001) 10. 0(. 0016)
364. 4(. 0001) 7.6(.0059)
on Final Step
F (P>4)
14. 2(. 0002)
694. 5(. 0001)
918. 7(.0001)
412. 7(. 0001)
42 .2 ( .0001)
15. 2(. 0001)
15. 3( .0001)
18. 8(. 0001)
11. 7(.0006)
7.6(.006)
F-Model - 36.4
R1 Model - .729
R2 Model
.712
.720
.724
.726
.728
.729
* variable descriptions can be found in Table 5.3
-------
4,J
Table 5.2
Regression Statistics for Final Logarithmic
Model
DEPZ = 60 + &2LNVWF + 33POTEMP
+ 05POTEMP*LNX + ggNSDSQ + B?BFDOWN
+ £8MD36 + B9MD5'OLNX +
Source
DF
Sum
of
Squares
F-Value (PR>F)
(SAS Type IV)
LNVWF
POTEMP
LNX
POTEMP*LNX
NSDSQ
BFDOWN
MD36
MD50LNX
W15
REGVAR
Model
Error
Total
Parameter
BO
P n
B3
B4
^6
B7
1
1
1
1
1
1
1
1
1
1
40
1327
2016
10 '
142
8
1438
Es timat e
2.2
6.
.192
• -.6
-1.4
12
8
.138
-.0
-.5
39
69
1.75
Bo -.124
3io
Bll
.0
.2
85
05
7
84
80
23
18
22
17
36
7499
26
101
31
30
T-Value
6
4
-6
-26
-33
-6
-3
3
-3
3
4
.50
.69
.62
.85
.08
.62
.61
.16
.47
.07
.45
(.
(.
(.
(.
(.
(.
(-
(.
(.
(.
(.
. 4
.9
. 8
.4
.7
.4
.4
.2
.4
.4
.4
.0
.4
(PR>T)
0001
0001
)
)
22.
720.
1094.
425.
43.
13.
10.
12.
9.
19.
407.
R2 =
Std
0
8
6
7
8
0
0
0
4
8
0
.
*
•
0001)
0001)
0001)
0001
)
•
0003)
0016
0005
0022
0001
)
)
)
)
.
•
4
•
( .0001
(.0001
(.0001
(.0001
)
)
)
)
(.0001)
(.0003)
(.0016
(.0005
(.0022
)
)
)
(.0001)
(.0001
.740
Error
347
040
023
045
007
006
157
554
036
027
046
)
* variable descriptions can be found in Table 5.3
-------
42
As in von Lindern (1980b) the best way to discuss these model results is in
terms of the regression coefficients. Parts of those discussions are in-
cluded here.
Ten variables were selected as important in predicting observed
cadmium levels. The first five variables comprise the initial surrogate
model. Five of the empirical modifiers offered were found to be significant.
Briefly, they are:
BFDOWN--the on-off indicator of blast furnace operation.
MD36 — an on-off indicator of the most severe limitation in mixing depth.
MD50LNX— the on-off variable for situation of uninhibited mixing depth
times the logarithm of distance.
W15--an indicator of suppressed wind speeds in the middle atmospheric
layers of the valley.
REGVAR—the severity code indicating adverse dispersal conditions asso-
ciated with peculiar synoptic situations.
The model as taken from the parent document is as follows:
Jn(xT). B B0 + B1 InQHF + 82 InVWF + B3 NSDSQ + 84 POTEMP
+ 85 LNX + 66 POTEMP * LNX + 8? BFDOWN + 6Q MD36
+ 69 MD50LNX + 61Q W15 + 8^ REGVAR
The parameters, their associated independent variable, and the
parameter values are shown in Table 5.3.
-------
43
Table 5.3 Parameter Values for the Logarithmic Model
Parameter Independent
Variable
BQ Intercept
B! In(QHF)
B2 In(VWF)
B3 NSDSQ
64 POTEMP
B5 LNX
Bfi POTEMP*LNX
B7 BFDOWN
Bg MD36
Bg. MD50LNX
B10 wis
0n- REGVAR
Factor P
Des crip tion
Initial dispersion
Source height function
Receptor View function
Lateral position factor
(Stability )
( and )
(distance factors)
Operations factor
Limited mixing depth fa
Uninhibited mixing dept
factor
Inhibited mid-level win
factor
Severe synoptic factor
arameter Value
2 .26
1.00
.192
-.039
-.612
-1.48
.138
-.569
ctor 1.75
h
-.124
ds
.085
.204
for a detailed description and derivation of these variables
please see von Lindern 1980b
-------
44
These parameters and associated variables can be grouped for discus-
sion relative to their contribution to quantifying pollutant dispersion in
this valley.
B B Be. and Bg are the dispersion parameters for the plume centerline
dilution effect associated with the mean wind as derived from the
traditional Gaussian form. These parameters were used to derive the
familiar o -o plots of Gifford (1961) and direct comparisons of the
dispersal conditions in this situation are made relative to the
standard modeling assumptions.
8-, is the unit coefficient for InQHF or the source strength-source
height term. This term reflects the initial source strength reduced
by a factor dependent on the relative source-receptor height. The
latter is developed from the same basic component parameters as
BO> 64, 65, and B6 above.
£>2 and B3 are associated with the terms ln(VWF)and NSDSQ. These two
variables, as they were developed, serve to mitigate the standard
model predictions with respect to the topographically induced wind
conditions. VWF is an exposure factor that accounts for the reduced
receptor "view" of the source associated with up- and down-valley
wind shifts. NSDSQ is associated with the lateral variance in the
mean wind and accommodates reduced exposures associated with the
cross-valley wind shifts. In the form offered in the modeling
analysis, they become empirical modifiers of the more traditional
dispersion equation characterized by the above parameters and
variables.
-------
45
B7 is the parameter for BFDOUN and is a direct empirical modifier asso-
ciated with shutdown of the largest single cadmium source. The 8
estimate for this term is (-.569). When the blast furnace is down
(BFDOWN = 1), the predicted effect is exp(-.569 * 1) = .57 times
the model prediction. This suggests that when the blast furnace is
nonoperative at least 16 hours per day, ambient cadmium levels are
reduced 43%.
Bg and Bg are associated with extreme mixing depths. Bg is the parameter
for MD50LNX. This variable allows for greater dispersion under
uninhibited vertical dispersion conditions. Because a limited
mixing depth associated with nocturnal inversion is the "normal"
situation in this valley, the standard diffusion parameters (BQ, B*,
Be. Bg) are calculated under that circumstance. The value of Bg is
0.125. The significance of this variable is as follows: when
mixing depth is great (i.e., MD50 = 1, MD50LNX = ln(x)) the value of
the coefficient of ln(x) or p + q = -(1.48 + .125) = -1.61. This is
nearly the value supposed in the traditional Gifford-Pasquill form as
discussed in von Lindern (1980b). This supports the idea that the
"normal" situation in the Silver Valley has an associated limit to
vertical dispersion probably related to the surface based nocturnal
inversions. Uninhibited vertical dispersion is an "abnormal" situa-
tion. Similarly, when mixing depth is severely inhibited, an oppo-
site "abnormal" effect is present. The variable MD36 has a value
^
of 1 when mixing depth is most shallow and 0 at other times. The 6
value for this variable is 1.75. This suggests that when the lowest
level inversion structure exists, the model predictions are increased
-------
46
by exp(1.75) = 5.75 times. This represents a severe condition
treated here by a simple empirical modifier.
6-.Q and 6,, are empirical modifiers associated with special synoptic
situations. Q in'MS accounts for reduced wind speeds in the mid-
level valley atmosphere. B-J-J is associated with REGVAR, an indicator
of severe synoptic conditions. The W15 variable has greatest
effect when the mean wind value is less than 1 mph. At that value
the model estimate may be increased as much as exp(.08*5) = 1.5
times. This situation implies extreme calm or shear in the middle
atmospheric levels. REGVAR is a severity code associated with some
peculiar synoptic conditions. Two conditions are especially
important. They are the valley drainage wind (=3) and stagnation
(=5) that are both associated with high pressure areas in the moun-
tain range vicinity. The former can increase model estimates by
exp(3*.20) = 1.8 times and the latter by exp(5*.20) =2.7 times.
The strength of the initial model indicates that the mountain valley
drainage phenomena dominate pollutant dispersion in the Silver Valley. The
two mixing depth variables selected in the stepwise process suggest that
nocturnal inversions are also part of the "normal" dispersion picture for
the valley. Four levels of mixing depth were offered in the stepwise pro-
cedure. The non-significance of the two middle levels indicates that they
are accounted for in the remainder of the model. In practical terms this
means that the basic source-receptor model reflects a diurnal capoing in-
version between 3600 and 4800 feet. Special modifiers to the basic model
are required only when greater or lesser mixing depths are present.
-------
47
It also means that the dispersion parameters derived from the re-
gression coefficients for this situation reflect this diurnal phenomenon.
Some important aspects of the montane air pollution meteorology for this
area can be explained by comparing these derived dispersion coefficient
estimates with the standard Gifford-Pasquill parameters.
Figures 5.1-a and b show the .derived dispersion parameters for this
situation plotted as solid lines. The dotted lines are the corresponding
plots taken from the subroutine distributed by EPA (1976b) to estimate
Gifford-Pasquill dispersion parameters. (The units have been converted as
indicated in the axes labels.)
In discussing the differences in these two sets of curves, it is
important to-remember that the standard curves represent the expected
standard deviations in the horizontal and vertical distributions of pollu-
tants calculated for different stability criteria and downwind distances.
Both sets assume a normal distribution around a plume center!ine defined by
the mean wind vector and have been developed from field observations over
flat terrain for relatively short averaging periods (<30 min).
The curves offered in this study are derived in a totally different
manner. The Gaussian form is present, but much modified in an effort to
accommodate the majority of wind fluctuations in the NSDSQ and VWF terms.
These two terms account for, respectively, the daily cross-valley variation
in wind direction and the variation in wind speed and flow up and down the
valley. In a sense they normalize the dispersion curves by accounting for
.*
the gross fluctuations related to the local wind phenomena.
The dependent variable in this model development was a twenty-four
hour average. As a result, all independent variables were constructed on a
-------
48
Figure 5.la Comparison of Standard and Derived
Horizontal Dispersion Parameter Estimates
10
25 50 100150
distance (.Imi.)
-------
49
figure 5.1b Comparison of Standard and Derived
Vertical Dispersion Parameter Estimates
10
.01
5 10
25 50 100150
distance (.ImiJ
-------
50
twenty-four hour basis. This represented no great inconvenience because the
mountain-valley drainage wind is a diurnal phenomenon. However, it is not
obvious what "the a and o terms in the above derivations and charts repre-
sent. Examination of Table 5.2 shows that a certain amount of the variance
in pollutant concentrations is explained by the gross wind variation terms,
NSDSQ and VWF. Other empirical factors related to operations and "abnormal"
meteorology explain a small percentage. However, the greatest portion of
the sums of squares is explained in the three terms from which the a and GZ
charts are derived. Those terms most likely represent the downwind pollutant
distribution for the component wind period, averaged over twenty-four hours.
The component averaging period is the one-hour mean wind. It is suspected
that the a and a values derived are the expected standard deviations in
downwind pollutant concentrations for one hour for a given stability cate-
gory. However, that value necessarily reflects an average for all the hours
of the day. This is a most important point to remember in discussing the
differences in these and the standard curves.
Turner (1979) pointed out that any modeling effort has to consider
the pertinent averaging period with respect to both the prevalent meteoro-
logical phenomena and the ambient standard in question. The same logic pre-
vails here. These charts and the other significant model variables can
illustrate many of the difficulties encountered in applying Gaussian form
models to complex terrain, provided the pertinent averaging time is con-
sidered.
There are three obvious differences in the form of these two sets
of curves. The first difference is that the estimates are similar for
unstable conditions but considerably less dilution occurs as neutral
-------
51
conditions are approached, and that effect is exacerbated toward stable
conditions. The second inconsistency is that the slopes of the curves in
the horizontal dispersion chart are notably less than their standard counter-
parts. The third difference is that under very stable conditions a nearly
uniform distribution in the vertical with downwind distance is predicted in
this study's o chart.
The mitigation of terrain effects under unstable conditions has been
noted by several researchers (Hinds, 1970; Fosberg et al., 1976; Reid, 1979).
It is likely that when unstable conditions prevail, no capping phenomena are
present and uninhibited vertical dispersion would persist. Similarly, the
tendency to develop calm and stable layers aloft is reduced over the twenty-
four hour period. As a result, the only inhibition to normal diffusion
present would be terrain channeling. Under unstable conditions terrain
channeling would likely exercise its influence in the horizontal, but not
for some distance downwind. The effect of terrain channeling on the hori-
zontal dispersion parameter may be seen in the reduced slopes noted above.
Other complex terrain researchers have made similar findings as reviewed by
Miller (1979). However, the result in these cases is usually curved a lines
starting out at or near standard slopes and decreasing in slope with distance.
This is, perhaps, a more appropriate form than that presented in this study,
as the effect of terrain channeling would become more pronounced with plume
growth relative to the valley width. Unfortunately, this model form can
only accommodate straight lines in the horizontal.
Essentially the mitigative effects of instability on complex terrain
dispersion may be accounted for in the absence of those phenomena that pro-
duce the confounding situations. As neutral conditions are approached the
-------
52
nocturnal inversion and drainage wind phenomena become routine. Over a
twenty-four hour period, a considerable period of time is spent under an
inverted temperature structure, downslope winds develop, and at least two
directional changes in valley flow occur. In addition, during inversion
breakup a double dosage of pollutants can occur. As stable conditions
develop these phenomena become more intense with increased duration. These
hours or, more appropriately, the dispersion observed in these hours is
included in the "average" that produces the above charts.
As very stable situations are encountered, calm conditions, intense
inversions aloft, and severe limitations in mixing depth are likely. Most
air quality models of the Gaussian form have recognized that, under limited
mixing depths, uniform vertical distribution may develop some distance down-
wind. That observation may be seen in the o curves at stable conditions.
Several researchers have noted that it is stable conditions that are
difficult to simulate in complex terrain situations. In this case, the fre-
quency and duration of particular phenomena (that become more frequent as
stability increases) are ultimately responsible. It seems that with the
inclusion of mitigating or normalizing variables that account for those
phenomena, and with proper consideration of the averaging period, the complex
terrain situation may be discussed in an empirical Gaussian format.
5.4 APPLYING THE BASIC SOURCE RECEPTOR RELATIONSHIP
5.4.1 Methodology. Thus far, the modeling procedure has concentrated on
defining the basic relationship between a receptor and a source. Having
developed a satisfactory model, the next step was to apply it to all the
source-receptor combinations in the valley. In practice this was a
-------
53
mammoth task. However, it was greatly facilitated by employing the Geo-
graphic Information System.- The details of this application are complex
and can be found in von Lindern (1980b).
As the relationship was applied to each source, the impact estimates
were accumulated by source category at each of the valley's nine monitoring
locations. These categorical estimates were then regressed against observed
ambient concentrations. This was accomplished for each day of the two-year
study and done simultaneously for lead, cadmium, and TSP. The result is a
calibrated model that reflects the most significant particulate sources and
weights the relative impacts of the various categories. This, again, was an
exhaustive and complex procedure that is detailed in von Lindern (1980b).
Over 4300 observations were analyzed. The regression statistics are shown
below.
The eight source categories are SMLOWEST (low-level smelter sources),
SMMIDEST (mid'level smelter sources), SMHIEST (smelter tall stack), ZPLOWEST
(zinc plant tall stack), AMP (ammonium phosphate plant), ACTEST (active
fugitive sources), and PASEST (passive fugitive sources). They are described
in detail in von Lindern (1980b) and the emissions inventory section of this
report. Four of these source categories were found to be significant in
predicting parti'culate concentrations in the Silvery Valley. They are low-
and mid-level smelter sources, and both active and passive fugitive sources.
The other source categories likely do contribute, but are insignificant in
magnitude when combined with these sources. Final prediction statistics can
•»
be found in Tables 5.4a and b. BKGROUND refers to TSP background levels
(BKGROUND = 0 for lead and cadmium).
-------
.54
Table 5.4a Regression Statistics for the Model:
TPOL - ^SMLOWEST +B2SMMIDEST + B-jSMHlHEST
+ B4ZPLOWEST + 65ZPHIHEST + B6AMP
-I- 0-PASEST + BgACTEST + BgBKGROUND
Source
DF
Sum of Squares
(SAS Type IV)
F-Value (PR>F)
SMLOWEST
SMMIDEST
SMHIHEST
ZPLOWEST
ZPHIHEST
AMP
PASEST
ACTEST
BKGROUND
Model
Error
Total
1
1
1
1
1
1
1
1
1
136963
27801
696
971
993
1196
52130
5246
576519
3771996
1527706
5299703
379. 9(. 0001)
77. 1(. 0001)
1.9(.1646)
2.7(.1008)
2.8(.0970)
3.3(.0685)
144. 6(. 0001)
14. 6(. 0001)
1598. 9(. 1646)
1162. 4(. 0001)
R2 - .712
Parameter
Estimate
T-Value (PR>|l|)
Std. Error
Si -A25
B2 4.62
B3 27.
B4 27.8
B5 -109.1
B, -7.16
B; 19-9
Bg 9.28
B9 35.5
19. 5(. 0001)
8. B(.OOOl)
-1.4(.1646)
-1.6(.1008)
-1. 7(.0970)
-1.8C.0685)
12. OC. 0001)
3.8( .0001)
40. OC- 0001)
.021
.53
5.14
17.0
65.7
3.93
2.43
1.65
.887
TPOL Total ambient participate concentration
SMLOWEST Low-level smelter sources estimated ambient impact
SMMIDEST Mid-level smelter sources estimated ambient impact
SMHIHEST Smelter tall stack estimated impact
ZPLOWEST Low-level zinc plant sources estimated impact
ZPHIHEST Zinc plant tall stack estimated impact
AMP Ammonium phosphate plant estimated impact
PASEST Passive fugitive sources estimated impact
ACTEST Active fugitive sources estimated impact
BKGROUND TSP estimated background concentration
-------
Table 5.4b Regression Statistics for the Final Relative
Source Impact Model:
TPOL = 31SMLOWEST + g2SMMIDEST +
+ 05BKGROUND.
55
Source
SMLOWEST
SMMIDEST
PASEST
ACTEST
BKGROUND
Model
Error
Total
Parameter
JJ!
f3
DF'
i
i
i
i
i
5
4310
4315
Estimate
.423
4.20
17.9
10.9
35.2
Sum of Squares
(SAS Type IV)
144129
33016
45310
7458
774061
3793243
1545150
5338393
T-Value (PR>|l|)
20. 1(. 0001)
9.6(.0001)
11. 2(. 0001)
4.6(.0001)
46. 5(. 0001)
F-Value (PR>F)
402. (.0001)
92 . (.0001)
126. (.0001)
21. (.0001)
2159. (.0001)
2116. (.0001)
R2 «= .711
Std. Error
.021
.437
1.59
2.39
.757
-------
56
This model in Table 5.4b was used to predict ambient concentrations
for each quarter in the study period. Those predictions can be found in
Appendix D and a quarterly summary is presented in Table 5.5. Thos.e pre-
dictions are used to evaluate relative source contributions to ambient lead
concentrations and to estimate required source reductions for achieving the
NAAQS. The individual source results and a "residuals" analysis can be
found in von Lindern (1980b). An observed/predicted concentrations summary
follows.
Model predictions for quarterly lead means range from .26 to 7.2
yg/m over the entire study area. Actual observed concentrations range from
.25 to 12.5 yg/m . These results are summarized in Table 5.5 along with the
ratio of predicted to observed values. This ratio on an annual basis con-
sistently falls between .5 and 2.0 (a kind of unofficial measure of model
quality). Moreover, on a quarterly basis the model does well in predicting
means for most of the stations. This is especially true in consideration
of the range of values and the fact that predictions are based on quarterly
mean emissions averages.
However, the model does characteristically underpredict in certain
situations. The most important are those where a few extremely high indivi-
dual readings inflate the observed mean. Many of these outlying values do
not seem to be meteorologically based, but do occur at different stations on
the same day and always downwind from the smelter. It is most likely that
these extreme values are the result of severe emissions excursions at the
^ i
smelter. .It is important to note that the model does not effectively pre-
dict the impact these days have on the quarterly means. This is important
both in terms of utilizing the model output and developing an attainment
-------
56
This model in Table 5.4b was used to predict ambient concentrations
for each quarter in the study period. Those predictions can be found in
Appendix D and a quarterly summary is presented in Table 5.5. Those pre-
-^
dictions are used to evaluate relative source contributions to ambient lead
concentrations and to estimate required source reductions for achieving the
NAAQS. The individual source results and a "residuals" analysis can be
found in von Lindern (1980b). An observed/predicted concentrations summary
follows.
Model predictions for quarterly lead means range from .26 to 7.2
yg/m over the entire study area. Actual observed concentrations range from
.25 to 12.5 yg/m . These results are summarized in Table 5.5 along with the
ratio of predicted to observed values. This ratio on an annual basis con-
sistently falls between .5 and 2.0 (a kind of unofficial measure of model
quality). Moreover, on a quarterly basis the model does well in predicting
means for most of the stations. This is especially true in consideration
of the range of values and the fact that predictions are based on quarterly
mean emissions averages.
However, the model does characteristically underpredict in certain
situations. The most important are those where a few extremely high indivi-
dual readings inflate the observed mean. Many of these outlying values do
not seem to be meteorologically based, but do occur at different stations on
the same day and always downwind from the smelter. It is most likely that
these extreme-values are the result of severe emissions excursions at the
^
smelter. It is important to note that the model does not effectively pre-
dict the impact these days have on the quarterly means. This is important
both in terms of utilizing the model output and developing an attainment
-------
Table 5.5 Summary of Mean Quarterly Ambient Air Lead Impact
Estimates, Predicted Values, and Observed/predicted Ratios
i
T
A
T
1
0
N
C
(1) A
T
P
(2) N
H
(3) H
V
(4) I
S
K
(5) H
C
K
(6) C
H
0
(7) S
B
W
(8) A
L
K
1CIOM
V «
KEY E OBSERVED -,
(RATIO) ug PB/m
PREDICTED
QUARTER
3-77
.47
(-5)
.91
2.33
(1.2)
1.94
6.9
(2.0)
3.4
12.5
(2.9)
4.4
6.5
(2.2)
2.9
4.1
(1.5)
2.7
1.5
(2.5)
.6
.9
(1.9)
.5
4-77
.86
(1.3)
.67
4.02
(3.4)
1.19
8.7
(2.2)
3.8
12.3
(1.7)
7.2
7.1
(2.2)
3.2
6.9
(2.1)
3.2
3.5
(6.4)
.6
1.8
(.5)
.3
1-78
.57
(.8)
.76
2.75
(1.6)
1.73
7.7
(1.8)
4.2
11.6
(3.9)
3.0
6.3
(1.5)
4.2
6.3
(1.6)
4.0
1.9
(2.2)
.9
.9
(1.6)
.6
.65
(l.S)
1. 33
2-78
.37
(.3)
1.22
.99
(-7)
1.50
3.4
(1.2)
2.8
6.2
(2.1)
3.0
2.4
(1.0)
2.5
2.9
(1.2)
2.4
.7
0.«)
.5
.5
(1.0)
.5
.31
.„ ( 5)
3-78
.20
(.2)
.89
.85
(.5)
1.62
2.4
(.8)
2.9
3.6
(1.0)
3.6
1.9
(.7)
2.7
1.4
(.6)
2.5
.4
(1.0)
.4
.3
(.80)
.4
.21
(.3)
4-78
.54
(1.0)
.56
2.81
(1.6)
1.75
6.1
(1.9)
3.3
6.0
(.9)
5.5
5.2
(1.4)
3.7
5.6
(1.6)
3.6
1.2
(1.8)
.7
.6
(2.1)
.3
1.12
, ,. O.o>
1-79
.60
(.6)
1.01
2.38
(-8)
3.08
4.8
(1.1)
4.2
5.5
(1.0)
5.7
4.5
(-7)
6.3
4.6
(.8)
6.0
1.9
(2.1)
.9
1.0
(2.7)
.4
.70
.., '-5>
2-79
.21
(.2)
1.13
.77
(.4)
1.88
2.6
(.8)
3.1
3.7
(.6)
6.2
2.5
(-5)
5.4
2.4
(.6)
3.8
.4
(.8)
.5
(1.0)
.3
.31
C.3)
01
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58
strategy. The effect of these days is great enough that they deserve
special treatment in attainment considerations; and they are separately
•discussed in the next section. What is important to remember, at this point,
is that the model predictions are based on average emission rates. The
resultant predictions are then "average predictions" and any attainment
strategy based on the model applies reductions to average emission rates.
These reductions will not guarantee compliance with the ambient standard
in and of themselves. Simultaneous control of the severe excursions must be
accomplished as well. Descriptions of the model results by station and
source category follow.
5.4.2 Model Results by Station
CATALDO. Observed ambient lead concentrations range from .008 to 2.8 yg/m
3
on a twenty-four hour basis. Quarterly means vary from .20 to .86 yg/m or
from 13 to 57% of the quarterly standard. Model estimates range from .56 to
1.13 yg/m . In general the model overpredicts concentrations for this site.
According to the model, passive sources are major contributors to lead levels
at Cataldo. Percentage contributions from the passive source category run
as high as 44% of the quarterly mean in the third quarter (July-September).
Summer quarterly mean passive estimates of .6 to .7 yg/m are predicted.
These are among the highest passive predictions for any site. The predom-
inant sources of this passive contribution seem to be alluvial deposits on
the nearby Mission Flats and along the river's flood plain north and east
of the townsite.
\
The remaining source contributions seem to be dominated by the mid-
level smelter contribution, 60%; low-level smelter sources, 12%; active
-------
59
sources, 6%. Passive sources amount to 20% on an annual basis. It is
suspected that, although not statistically significant, the tall stack emis-
sions constitute an important portion of the mid-level impact at this
distance.
KINGSTON. Quarterly means for lead are observed from .21 to 1.11 ug/m .
Model predictions are from .77 to 1.33 ug/m . The model suggests lead levels
observed here are 80% resultant of mid-level smelter emissions, 20% low-
level. There are no significant passive or active sources of lead affecting
the Kingston location according to this model.
WALLACE. Quarterly means for lead for Wallace range from .25 yg/m to 1.8
ug/m observed values. Model estimates vary from .26 to .47 ug/m . The
maximum does exceed the proposed 1.5 ug/m limit. However, that exceedance
can be traced to an extraordinary single observation of 6.625 ug/m that
occurred on 12/21/77. Passive source estimates run as high as .21 ug/m and
can account for up to 32% of the total exposure on a quarterly basis. On an
annual basis it is suspected that tall stack and mid-level emissions combine
for 75% of the total exposure, low-level smelter sources contribute 14%,
passive local sources (primarily north of the city) 11%, and there is no
significant active lead source in Wallace.
OSBURN. Quarterly mean lead levels in Osburn range from .42 to 3.54 ug/m
while model estimates range from .49 to .94 ug/m . The 3.54 u9/m quarterly
average observed in the fourth quarter of 1977 can be traced in great part
to a single daily observation of 20.4 ug/m (the same day that an
-------
60
extraordinarily high value was observed in Wallace). Similarly, the proposed
standard exceedances that were observed in three other quarters can be traced
to single extraordinary days. If those extraordinary days are ignored,
Osburn seems to be in compliance with the standard. Both passive and active
source contributions seem minor, amounting to only 7% of the total impact
in summer months. On an annual basis, high and mid-level sources account for
80% of the predicted impact, low-level 16%, passive 3%, and active sources 1%.
PINEHURST. Observed quarterly means range from .77 to 4.02 yg/m3. Model
estimates range from 1.19 to 3.08 yg/m . Although single maximum values
constitute a large part of the quarterly average, Pinehurst does seem to be
a bona fide noncompliance area even in ignorance of these values. Passive
sources can account for up to 19% of the total lead levels observed here or
a maximum of .37 yg/m . Active sources are absent. On an annual basis low-
level smelter sources constitute 20% of the total, mid-level 70%, and passive
10%.
KELLOGG CITY HALL. Quarterly means ranging from 1.4 to 6.9 yg/m are observed
at Kellogg City Hall. Model predictions ranged from 2.5 to 6.0 yg/m . Pas-
sive and active sources combined constitute at the most a 7% contribution to
these levels. On an annual basis mid-level sources contribute 66% of the
impact, low-level sources 30%. The maximum quarterly mid-l:evel impact is
3 3
estimated at 3.41 yg/m , the maximum low-level impact is 2.52 yg/m .
KELLOGG MEDICAL CENTER. Observed levels at KfC range from 1.9 to 7.1 yg/m ;
model estimates range from 2.7 to 6.3 yg/m . Although these levels are
-------
61
similar to those at Kellogg City Hall, it seems that both passive and active
sources make a greater contribution to total impact at this station. Passive
source contributions are estimated as high as .74 yg/m , active sources as
3
high as .36 yg/m . The principal passive sources seem to come from the air-
port and tailings pond bank areas. Principal active sources are roads and
transportation facilities in the vicinity of the smelting and milling com-
plex, and sinter-storage activities. Combined, passive and active contribu-
tions can constitute as much as 1.1 yg/m quarterly mean or 14% of the total
estimate in summer. On an annual basis passive and active sources constitute
7% of total impact, mid-level smelter sources 59%, low-level 35%. Maximum
3 3
mid-level smelter contribution is 3.48 yg/m , maximum low-level 2.69 yg/m .
These do not occur simultaneously with each other or the passive-active
maxima.
SMELTERVILLE. Quarterly mean lead levels in Smelterville range from 2.41
3 "3
to 8.56 yg/m . Model estimates range from 2.80 to 4.80 yg/m . Both this
station and Silver King School suffer from a number of extraordinarily high
individual readings that cause the actual resultant means to be higher than
model predictions in most quarters. Passive sources can be significant
amounting to 15% of the summer total or as high as .37 yg/m . Active source
estimates go as high as .13 yg/m but amount to less than 5% of the total
impact in most quarters. Passive sources surround Smelterville and impact
from all directions. However, the McKinley Avenue area and smelter active
sources contribute the bulk of the active source contribution. Maximum
quarterly mean mid-level estimate is 2.24 yg/m , maximum low-level is 2.92
yg/m . Mean annual relative contribution is 54% low-level sources, 37%
mid-level, 2% active, and 7% passive.
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62
SILVER KING SCHOOL. The highest concentrations of heavy metals occur at this
station. Quarterly means range from 3.6 to 12.5 yg/m , while model estimates
range from 3.0 to 7.2 yg/m . Larger means are underpredicted because of the
effect of the extreme individual readings that occur in several quarters.
Passive contributions are estimated as high as .6 yg/m , active contributions
as high as .58 yg/m . They can amount to as much as 30% and 16%, respec-
tively, of the total quarterly estimate. Their simultaneous maximum is 1.0
yg/m amounting to 28% of that total quarterly estimate. Low smelter sources
dominate the estimates at Silver King School, amounting to 77% of the total
on an annual basis, mid-level smelter sources account for 6%, active 4%, and
passive sources 13%. Quarterly mean estimates attributable to low smelter
sources are as high as 6.53 yg/m , mid-level sources only as high as .2 yg/m3.
5.4.3 Model Results by Source Category. There is little effect of stack
height associated with low-level smelter emissions. As a result they exert
their predominant effect close to the smelter and decrease rapidly with
distance. Mid-level sources, on the other hand, have little effect within
one-half mile of the smelter. As distance increases the relative impact
of the mid-level sources increases markedly. Figure 5.2 shows the relative
impact of low and mid-level sources, by station, on an annual basis. Figure
5.3 shows the actual estimate components at each station on an annual basis.
Active sources exert small effects on estimates at several stations.
They seem to be important, in terms of attainment strategy, only during
•/
summer quarters at Silver King School, Kellogg Medical Center, and Smelter-
vine. At these locations the active impacts can be traced to transportation
activities around the smelter and McKinley Avenue area, and sinter product
handling in areas peripheral to the smelter.
-------
Figure 5.2 Percent Relative Impact Estimates for Ambient Lead
by Source Category at each Monitoring Location
iOO
% ANNUAL
50
LEAD IMPACT
CAT KIN PNH SWiV SKS KMC KCH OSB WAi
Source Categories:
active
passive
mid smelter
low smelter
co
-------
Figure 5.3 Predicted Ambient Lead Impact Estimates by Source
Category at each Monitoring Location
3
ug
m3
active
passive
mid smelter
low smelter
CAT
PNH SMV SKS KMC KCH OSB
Source Impact Estimate Components
CTt
-------
65
Passive sources exert significant impact at several monitors. In
terms of percentage impact, Cataldo is most affected as a result of high lead
alluvium deposited across the flood plain and the Mission Flats. In concert
with other sources, however, consideration of the passive sources is most
important at Silver King School, Smelterville, and Kellogg Medical Center.
Both Smelterville and Silver King are surrounded by numerous high lead pas-
sive sources, especially in the immediate vicinity of the smelter. Kellogg
Medical Center is exposed in the predominant wind direction to the airport
area and the massive tailings pond impoundment area and features.
5.5 DISCUSSION OF MODELING RESULTS
Perhaps the most important way to discuss the modeling results is in
terms of seasonal impacts of the different source categories. In analyzing
the basic source receptor relationship earlier, it was evident that certain
meteorological conditions are critical in determining dispersal conditions
and ambient concentrations in the Silver Valley. Because these conditions
vary with season and because the NAAQS is a quarterly standard this becomes
an extremely important consideration. Table 5.6 shows the major components,
critical season, and impact area and ambient impact estimate for each
significant source category. It is evident that the critical seasons for
the several source categories do not coincide. Low- and mid-level smelter
sources have their maximum impact under stable conditions exacerbated by
light winds ^and high pressure synoptic patterns that inhibit dispersion.
These conditions prevail in the late fall and winter. Active fugitive
sources, on the other hand, have their maximum impact under unstable condi-
tions with light winds in the absence of moisture. This type of weather
-------
Table 5.6 Source Categories' Critical Seasons and Impact Areas
Source category
Low- level smelter
Mid- level smelter
Active futitive
Largest
component
sources
Lurgi 50%
OrePrep 35%
Crushing 10%
Blast furnace 55%
Pellet dryer 25%
Building vents 10%
Smelter roads
McKinley Avenue
Sinter handling
Critical
season
(quarter)
Fall , winter
(4,1)
Winter
(1)
Spring, summer
(2,3)
Critical
impact
area
< 1 mi.
Silver King,
Smelterville
2-4 mi.
Kellogg,
Pinehurst
< 1 mi.
NW Kellogg
Silver King
Maximum* ambient
-estimate
ug Pb/m quarterly mean
7.0 ug/m3
SKS
~
= 3.5 pg/m
* KMC, KCH
* 2.5 yg/m3 PNH
.6 pg/m3 SKS
.4 yg/m3 KMC
~
Passive fugitive
Airport
Smelter property
Fairgrounds-
lumberyard
Summer, fall
(3,4)
< 1 mi.
Smelterville,
Silver King,
NW Kellogg
.6
SKS
.4 pg/m SMU
.75yg/m3 KMC
en
cr>
-------
67
occurs in the spring and early summer. Finally, passive fugitive sources
are active at neutral conditions with dry surface conditions and high winds.
This weather occurs in the summer and early fall.
This situation has a tremendous impact on any strategy developed to
meet the NAAQS. Table 5.7 examines the model estimates for the several
non-attainment monitor locations. It can quickly be seen that the worst
case situation for each of these locations occurs in the late fall and
winter. Further, it is evident that the impacts during this period are
nearly exclusively due to low- and mid-level smelter sources. Active and
passive fugitive sources are, for all practical purposes, absent during
this season.
There are some important conclusions that can be drawn at this
point:
1. As the critical impact season for the non-attainment area occurs in
the winter when active and passive sources are practically absent,
the control strategy for this season must be aimed at the smelter
sources.
2. As the primary impact areas for low- and mid-level smelter sources
do not coincide, both must be reduced significantly in meeting the
NAAQS.
3. A significant question remains as to the combined effect of smelter
sources and passive and active fugitive sources in the summer months.
Will the smelter source reductions required to meet the NAAQS in the
winter be sufficient to guarantee the standard in the summer when
combined with the active and passive source contributions?
The next section of this report deals with this most difficult
question.
-------
Table 5.7 Critical Quarters and Principal Sources for Non-Attainment Monitors
Location
Pinehurst
Smelterville
Silver King
Kellogg
Medical Center
Kellogg
City Hall
Maximum ambient
cone, ug Pb/m3
quarterly mean
3.1 ug/m3
4.2 pg/m3
7.2 pg/m3
6.3 pg/m
6.0 pg/m3
Critical
season
(quarter)
Winter
(1)
Winter
(1)
Fall
(4)
Winter
(1)
Winter
(1)
Component
Low- level
.9 pg/m3
(18%)
2.9 pg/m3
(69%)
6.9 ug/m3
(94%)
2.6 ug/m3
(.23%)
2.1 ug/m3
(24%)
sources
Mid-level
2.2 ug/m3
(82%)
1.2 ug/m3
(29%)
.1 ug/m3 .
(2%)
3.5 ug/m
(75%)
q
3.4 ug/m
(76%)
Ambient impact
(% max. impact)
Active Passive
0 0
— —
3 3
<.l ug/m <.l ug/m
(1%) (1%)
. 1 ug/m . 1 pq/m
(2%) (2%)
3 3
.1 ug/m <.l pg/m
(1%) (1%)
0 0
GO
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69
6.0 STRATEGIES FOR ATTAINING THE NAAQS
6.1 ATTAINMENT CURVES
In this modeled representation, the primary sources of lead impact
have been grouped into four categories. Even with this considerable simpli-
fication, development of a sufficient attainment strategy will be difficult.
Each of the four main source categories can make a significant contribution
to ambient levels when applied to the 1.5 ug/m proposed standard. Moreover,
those meteorological situations that cause the greatest source impact for
one category are not necessarily the most significant in other categories.
As a result, great care must be exercised in selecting the proper conditions
under which to evaluate an attainment strategy.
In order to determine those combinations of source reductions that
will result in attainment of the 1.5 ug/m standard, the concept of the
"limiting situation" must be introduced. The "limiting situation" is that
period (quarter) and location (monitor) that requires the greatest source
reduction to meet the proposed standard. The "limiting situation" for any
source category is determined not only by the absolute magnitude of that
source estimate, but also the relative magnitudes of the other source cate-
gories in that same period.
Achieving the proposed standard under the modeled representation
requires th*t the following constraints be true under the "limiting situa-
tion" for each source.
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70
0 - CLQW) * (SMLOWEST) + (1 - CMID) *< (SMMIDEST)
+• (1 - CpAS) * (PASEST) + (1 - CACT) * (ACTEST) < 15.
SMLOWEST = quarterly impact estimate of low smelter sources
SMMIDEST = quarterly impact estimate of mid smelter sources
PASEST = quarterly impact estimate of passive sources
ACTEST * quarterly impact estimate of active sources
C.LOW = fractional reduction of low smelter sources
CMID = fractional reduction of mid smelter sources
CpA2 = fractional reduction of passive sources
CACT = fractiona^ reduction of active sources
It follows that constraints for each source category can be
developed as shown:
(1 - CLQW)( SMLOWEST) < 1.5 - (1 - C,^) (SMMIDEST)
' (1 " CpAS) (PASEST) - (1 - CACT) (ACTEST) or
r >T . H-5) , t n c ) (SMMIDEST)
LLOW ^ ' (SMLOWEST) u TIID' (SMLOWEST)
+ 0 - CpAS) (SMLOWEST) + ^ " CACT^ (SMLOWEST) and>
r >i . n.5) . n c ) (SMLOWEST)
CMID ^ " (SMIDEST) u ULOW; (SMMIDEST)
. (ACTEST)
;
_
PAS (SMMIDEST) ACT (SMMIDEST)
-------
71
r ii n._5) , n r } (SMLOWEST)
CPAS * ' " (PASEST) U " LLOW' (PASEST)
-. M r \ ^MMIDEST) . n c (ACTEST)
^ U " CMID; (PASEST) U ' LACT (FASTEST)
r >i . (1.5) , n c } (SNLOHEST)
LACT * ' " (ACTEST) l ' ULOW; (ACTEST)
()
(PASEST)
(ACTEST)
These equations can be used to determine the required control for
any source category by defining a control regime for the other three cate-
gories. Given a proposed control regime for three of the sources, the
appropriate equation is solved for each of the monitors for each quarter in
the study. The maximum value of C for the fourth source as determined by
this method then represents the "limiting situation" for that control
strategy. If that maximum value exceeds 1.0, then attainment of the ambient
standard is impossible, and further controls must be imposed on at least one
of the other sources. By iterating this process for all interesting control
strategies under all possible situations, attainment curves can be developed
that illustrate those combinations of source reductions capable of achieving
the proposed standard.
A primary concern of this study has been to evaluate the "background1
contribution to ambient lead levels at various locations in the valley and
how those "background" contributions can affect an attainment strategy for
the area. First, a definition of background is in order. If "background"
means lead levels in the absence of industrial activity, then they are
probably best represented by passive source category. Active sources,
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72
although they are akin to industrial activity, are not easily addressed in
an attainment strategy. The primary active source contributors to lead
levels are materials handling of smelter by-products and intermediates in
areas outside the smelter proper, and dusts raised by transportation activ-
ities in the vicinity of the smelter. The exact regulatory mechanism to be
employed in reduction of these sources is unclear.
It is likely that the eventual definition of "background" will be the
passive source contribution plus some fraction of the active source contribu-
tion. Figure 6.1 shows the results of solving the constraint equations for
the limiting situations assuming no control in passive sources and 0, 25,
50, 75, and 100% control of active sources. The abscissal and ordinal
values on this graph indicate the minimum combinations of low- and mid-level
source reductions required. For example, given no passive control and 50%
active control, possible minimal control requirements are found along the
50% line on the graph (e.g., LOW = 90, MID = 84; LOW = 82, MID = 90). Com-
binations of low- and mid-level control above and to the right of the solu-
tion line will achieve the standard; those to the left and below the line
will not.
The vertical line in Figure 6.1 shows that no matter how much control
is exercised in the active and mid-level source categories, at least 80%
low-level control is necessary. This is figured assuming no passive con-
trol. However, even if 100% passive control is assumed, solving the CLQW
equation for the "limiting situation" yields a control requirement of 78%.
The "limiting situation" in this case is the second quarter of 1978 at
Silver King School. Knowing that at least 78% control of low-level sources
will be required regardless of other source reductions, the constraint
-------
Figure 6.1. Attainment Curve for No Improvement in the
Passive Source Contribution
100
R
E
0
u
C
T
I
0
N
R
E
Q
u
I
R
E
0
I
N
H
I
0
L
E
¥
E
I
I
M
I
S
S
I
0
N
S
90
80
70
60
50
ACTIVE CONTROL
REQUIRED CONTROLS NECESSARY
TO ACHIEVE PROPOSED LEAD
STANDARD ASSUMING NO
PASSIVE SOURCE IMPROVEMENT
60
70
60
90
100
I REDUCTION REQUIRED IN LOW LEVEL EMISSIONS
•vl
CO
-------
74
equations are solved assuming 80, 85, 90, 95, and 100% low-level control.
One set of attainment curves was then developed for each of these low-level
control strategies. That family of curves is shown in Ftgures 6.2a through
6.2e. In these figures mid-level control requirements are plotted against
a supposed passive source reduction; the lines themselves represent a con-
stant level of active source reduction. As with Figure 6.1, values above
and to the right of a subject line will result in compliance; those to the
left and below will not.
Any combination of source reductions in the four categories can be
evaluated by these curves. For example, suppose no additional control on
active or passive sources is contemplated, and 90% control of low-level
sources is proposed. From Figure 6.2c, it is determined that 91% control of
mid-level sources will be necessary. Or suppose 50% active and 25% passive
control were feasible but only 80% low-level control were proposed. Under
this proposal Figure 6.2a shows that 84% mid-level control would be required.
6.2 EXTREME EXCURSIONS
The inability of this model to predict extreme values at all stations
has been discussed. Although that inability is not thought to affect the
relative impact calculations for mean emissions, these particular days must
be considered in an attainment strategy because of their effect on the
quarterly arithmetic means. These days were identified and examined sep-
arately. The results of that examination can be found in Table 6.1. The
data for each extreme day were qualitatively examined and each day was
assigned to one of five categories. Those categories were determined by
four factors found common to several of these excursions. The first
-------
Figure 6.2a. Attainment Curve for 80% Improvement in
Low-level Smelter Emissions
R
E
0
U
c
T
I
0
N
R
E
Q
U
I
R
E
0
1
N
H
1
D
L
E
V
E
L
E
H
I
S
s
1
0
N
S
100
90
eo
70
60
50
i
MM:
' ! ! ! I M I
OS ACTIVE CONTROL
1001
j ' i. j i REQUIRED CONTROL
i !
20 40 60
X JifillWSXJUlll PROPOSED IN
80
! COMBINATIONS NECESSARY
ASSUMING 802
LOW LEVEL EMISSION
REDUCTION
100
en
-------
Figure 6.2b. Attainment Curve for 85% Improvement in
Low-level Smelter Emissions
R
E
0
U
C
T
I
0
N
R
E
Q
u
1
R
E
D
I
N
H
I
D
L
E
V
E
L
I
H
I
S
s
I
0
N
S
100
90
80
70
60
50
t i
REQUIRED CONTROL
COMBINATIONS NECESSARY
ASSUMING BS%
LOU LEVEL EMISSION
REDUCTION
40
60
80
100
% REDUCTION PROPOSED IN PASSIVE SOURCES
-------
Figure 6.2c. Attainment Curve for 90% Improvement in
Low-level Smelter Emissions
R
E
0
U
C
T
I
0
N
R
t
q
u
i
R
E
0
I
N
N
I
D
L
E
V
E
L
E
H
I
S
S
1
0
N
S
100
90
80
70
60
lit i ;
• : • • |
01 ACTIVE CONTROL
REQUIRED CONTROL
COMBINATIONS NECESSARY
ASSUMING 90S
LOW LEVEL EMISSION
REDUCTION
50
20 40 60 80 100
X REDUCTION PROPOSED IN PASSIVE SOURCES
-------
Figure 6.2d. Attainment Curve for 95% Improvement in
Low-level Smelter Emissions
100
R
E
0
U
C
T
I
0
N
R
E
Q
U
I
R
E
0
I
N
H
I
D
L
E
V
E
L
E
H
I
S
S
I
0
N
S
90
BO
70
60
50
REQUIRED CONTROL
COMBINATIONS NECESSARY
ASSUMING 951
LOW LEVEL EMISSION
REDUCTION
20
40
60
80
100
oo
REDUCTION PROPOSED IN PASSIVE SOURCES
-------
Figure 6.2e. Attainment Curves for Total Elimination
of Low-level Smelter Emissions
100
R
E
0
U
c
T
1
0
N
R
C
Q
U
1
R
E
D
I
N
H
I
0
L
E
V
E
L
E
H
I
S
s
I
0
N
S
90
80
70
60
• i
01 ACTIVE CONTROL
REQUIRED CONTROL
. COMBINATIONS NECESSARY
ASSUMING 1001
LOU LEVEL EMISSION
REDUCTION
SO
20 40 60 BO 100
X REDUCTION PROPOSED IN PASSIVE SOURCES
-------
Table 6.1. Tabulation of Possible Causes for Extreme
Air Quality Excursions
SUSPECT
CAUSE
DRAINAGE WINDS
STAGNATION
LOW WIND SPEEDS
STRIKE/STARTUP
UNACCOUNTED
f DAYS
T
SUM H D
E A
MEAN S Y
E S
f DAYS
A
SUM L D
L A
MEAN Y
S
STATION
CAT
0
2
2
1
1
4
11.4
2.9
165
74.7
.45
KIN
0
3
0
0
2
5
20.9
4.2
140
76.1
.54
PNH
1
7
1
8
8
25
163.4
6.5
150
273.7
1.8
SMV
3
7
2
6
6
24
369.8
15.4
169
805.3
4.8
SKS
2
6
4
3
6
21
485.1
23.1
167
1093.3
6.6
KMC
0
4
1
1
11
17
233.1
13.7
164
659.6
4.0
KCH
0
3
1
2
6
12
211.5
17.2
145
614.2
4.2
OSB
0
2
2
1
9
14
100.3
7.2
172
222.0
1.3
UAL
0
2
1
0
9
12
42.1
3.5
161
111.6
.7
EAST
0
11
5
4
35
55
587
10.7
642
1607
2.5
WEST
6
25
7
18
23
79
1050.6
13.3
791
2323
2.9
TOTAL
6
36
12
22
58
134
1626.2
12.1
1433
3930
2.7
CO
o
-------
81
condition was several days of extreme readings during the strike of 1977,
and the startup period following for which no meteorological data were
recorded. The second factor included days of extreme atmospheric stability
or stagnation. The third situation was severe synoptic drainage winds
affecting stations to the west of the smelter, and the last included periods
of low wind speeds in the middle atmospheric levels. No meteorological or
operational factor could be found to explain the extreme values on the
remainder of the excursion days.
Three of these factors are accounted for to some degree in the model.
The stagnation and drainage wind regimes and low wind speeds in the middle
atmospheric levels were empirical qualifiers in the regression equation.
Passive source estimates predict significant impacts with drainage wind, and
extreme stability is treated by nearly a constant dispersion parameter.
These factors, however, do not predict as high an impact as was observed.
Perhaps the remaining two categories can suggest why. During the strike
the smelter was operated intermittently by salaried personnel and consider-
able construction of pollution control facilities was carried on by outside
contractors. Following resolution of the labor difficulties, new pollution
control facilities were in use. It may be possible that less than efficient
operation was practiced during these periods resulting in exaggerated emis-
sion rates. Furthermore, those days for which no meteorological explanation
of the high values can be found are likely caused by abnormal emissions.
Additional investigation of company operation records and upset reports may
be worthwhile in establishing this point.
The remainder of the discussion here will contend with the impact,
rather than the cause, of these days. The top part of Table 6.1 shows the
-------
82
number of extreme days In each category-for each station, for each direction,
and total tabulation. It can be noted that to the west of the smelter about
8% of the severe days can be attributed to drainage winds, 32% to stagnation
and extreme stability, 9% to low wind speeds, 23% to the strike/startup
period, and 33% are unknown. In the easterly direction drainage winds have
no effect as the monitors are upwind of the smelter. A similar percentage
of extremes were attributed to low wind speeds and extreme stabilities. The
strike period, however, did not seem to affect eastern stations to the degree
that occurred to the west. This may be attributable to less than twenty-four
operations of the smelter and the wind direction associated with the opera-
tion shift. The suspected cause of over 60% of the extreme concentrations
east of the smelter is unknown.
The lower part of Table 6.1 shows the effect of these days on the
total impact measured at each station. The number of extreme days at each
monitor is shown together with the mean and total lead impact. These values
are compared to those "for all days. This shows that, at non-attainment
stations, from 8 to 17% of the days account for 35 to 60% of the total impact.
These days have about 4 times more impact on the quarterly mean than does
an average day.
Any attainment strategy must consider these days and their extraor-
dinary impact. Deductive reasoning concludes that those severe excursions
whose cause cannot be found in the meteorology are likely due to excess
smelter emissions. .These excess emissions are, in turn, likely due to
•» •* ••
upsets, malfunctions, and startup/shutdown conditions. Further work in this
area and the development of an effective program for preventing or minimizing
is requisite to formulation of a viable attainment strategy.
-------
83
6.3 DISCUSSION
There are numerous combinations of source reduction scenarios that
will achieve the national standard according to results of this study.
However, there are certain minimum requirements and generalizations that can
be made. In order for any attainment strategy to be successful, the extreme
excursions suspected resultant from process upsets, malfunctions, and
startup/shutdowns must be addressed. Either additional studies should be
undertaken to confirm the cause of these high concentrations, or a compre-
hensive program and accompanying regulation controlling these situations
should be established.
Given that severe excursions are successfully addressed, the attain-
ment curves can be used to ascertain further requirements. Under any circum-
stances at least 78% reduction in low-level emissions will be necessary.
Assuming an 85% reduction in low-level emissions, significant improve-
ments in ambient concentrations can be achieved by reducing the active and
passive components. Remembering that Kellogg Medical Center is the limiting
situation regarding passive and active sources, improvements in those sources
affecting this monitor may be worthwhile. Those sources are sinter product
handling outside the smelter, McKinley Avenue, the airport area, the fair-
grounds/lumberyard area, and the tailings pond embankment area. With signifi-
cant improvement in these areas and 85% reduction of low-level sources, per-
haps an 80 to 85% control in mid-level sources would be necessary.
Examination of Table 4.3 shows that blast furnace upsets are the
dominant source in mid-level emissions. This same source is suspected as
being the largest contributor to the startup-upset extreme excursions. If
these upsets were eliminated, achievement of the standard would likely be
much easier.
-------
84
One additional point should be considered in developing an attainment
strategy. All the attainment curves are based on at least a 78% low-level
emission reduction required at Silver King School. Were the smelter owner
to purchase all property between the Smelterville monitor and the plant,
this requirement might be significantly reduced.
All these factors deserve consideration. However, in general, given
the current conditions, meeting the national standard will require:
i
1. A comprehensive control program for upset, malfunction, and
startup/shutdown situations.
2. Eighty to 85% reduction in low-level and mid-level emissions through
elimination or rerouting discharges to the tall stack.
3. Moderate effort to reduce active emission arising from by-product
handling outside the smelter, company roads, and McKinley Avenue.
4. Stabilization, covering, or revegetation of bare soils' around
Linfor Lumber, Smeltervilie fairgrounds, the airport, and the
smelter tailings pond embankments.
-------
85
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Unpaved Roadways. Atmos. Envir. 13(5):661-669.
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Calder, K. 1973. On Estimating Air Pollution Concentrations from a Highway
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deNevers, N., and Morris, R. 1973. Rollback Modeling—Basic and Modified.
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Illinois.
Fosberg, M. A., et al. 1976. Non Turbulent Dispersion Processes in Complex
Terrain. Atmos. Envir. 10:1053-1055.
Heichel, G. H., and Hankin, L. 1976. Roadside Coniferous Windbreaks as
Sinks for Vehicular Lead Emissions. J. Air Pollut. Control Assoc.
26:767-770.
*
Hinds, W. T. 1970. Diffusion over Coastal Mountains of Southern California.
Atmos. Envir. 4:107.
Idaho Dept. of Health and Welfare. 1974a. Survey of Heavy Metal Contam-
ination: Shoshone County, Idaho. Status Report, October 2, 1974,
Idaho Dept. of Transportation. 1974b. Highway Planning and Programming
Section, Personal Correspondence, October 1974.
Idaho Dept. of Health and Welfare. 1975a. Unpublished soil metal concen-
tration data from the summer of 1974. Division of Environment,
Coeur d'Alene, Idaho.
Idaho Dept. of Health and Welfare. 1975b. Water Quality Status Report,
South Fork Coeur d'Alene River Basin. Coeur d'Alene, Idaho,
March 1975.
Idaho Dept. of Health and Welfare. 1976a. Intensive Survey South Fork Coeur
d'ATene River. Boise, Idaho, May 1976.
Idaho Dept. of Health and Welfare. 1976b. Unpublished soil metal concentra-
tion data from the summer of 1975. Division of Environment, Coeur
d'Alene, Idaho.
-------
86
Idaho Dept. of Water Resources. 1976c. Population and Employment Forecast,
Series 1. Center for Research, Grants, and Contracts, July 1976.
Idaho Dept. of Health and Welfare. 1978a. Rules and Regulations for the
Control of Air Pollution in Idaho. Statehouse, Boise, Idaho.
Idaho Dept. of Health and Welfare. 1978b. Unpublished soil metal concen-
tration data from the summer of 1978. Division of Environment,
Silver-ton, Idaho.
Idaho Dept. of Health and Welfare. 1979. Silver Valley Non-Attainment
Area Achievement of the Total Suspended Particulate National Ambient
Air Quality Standards. Report by Region I, Division of Environment,
Coeur d'Alene, Idaho, April 20, 1979.
Little, P., and Wiffen, R. D. 1978. Emission and Deposition of Lead from
Motor Exhausts—II. Airborne Concentration, Particle Size and
Deposition of Lead near Motorways. Atmos. Envir. 12:1331.
Miller, C. W. 1978. An Examination of Gaussian Plume Dispersion Parameters
for Rough Terrain. Atmos. Envir. 12:1359-1364.
NOAA. 1976. Climatological Data. Annual Summary for Idaho. U.S. Department
of Commerce Publication.
PEDCo. 1973. Investigation of Fugitive Dust-Sources, Emission, and Control.
Contract No. 68-02-0044, Task Order No. 9, EPA, Research Triangle
Park, North Carolina.
PEDCo. 1974. Investigation of Fugitive Dust, Vol. I/II, EPA—450/3-74-036-
a/b. Environmental Report for U.S. EPA, Office of Air Quality
Planning and Standards, June 1974.
PEDCo. 1975a. Airborne Lead from Fugitive Dust Area Sources in the Silver
Valley. Testimony Support Document, September 22, 1975, BHS-75-1.
PEDCo. 1975b. Airborne Lead Emissions from Motor Vehicles in the Silver
Valley. Testimony Support Document, September 22, 1975, BHS-75-2.
PEDCo. 1975c. In Plant Fugitive Dust Emission Measurements—Bunker Hill
Lead Smelter. Testimony Support Document, September 30, 1975,
BHS-75-3.
PEDCo. 1979. Fugitive Testing Report—Bunker Hill Lead Smelter Blast Furnace
Operation, Smelterville, Idaho. EPA Contract #68-01-4147 TN85.
Report to EPA, Region X, Seattle, Washington.
PES (Pacific Environmental Services). 1979. Studies of Air Quality in Silver
Valley, Idaho: Estimates of Aira Source Emissions of Particulate
Matter and Lead; Air Quality Modeling Using VALLEY Model. Report to
the U.S. EPA, Contract No. 68-02-2536, April 9, 1979.
-------
87
Reid, J. D. 1979. Studies of Pollutant Transport and Turbulent Dispersion
over Rugged Mountainous Terrain near Climax, Colorado. Atmos.
Envir. 13(l):23-29.
Sehmel, G. A. 1973. Particle Resuspension from an Asphalt Road Caused by
Car and Truck Traffic. Atmos. Envir. 7:291-309.
Sistle, A., et al. 1979. A Study of Pollutant Dispersion near Highways.
Atmos. Envir. 13:669.
Smith, W. H. 1971. Lead Contamination of Roadside White Pine. For. Sci.
17:195.
Smith, W. H. 1976. Lead Contamination of the Roadside Ecosystem. J. Air
Pollut. Control Assoc. 26:753-766.
Turner, D. B. 1979. Atmospheric Dispersion Modeling—A Critical Review.
J. Air Pollut. Control Assoc. 29:902-941.
U.S. Environmental Protection Agency. 1969. Workbook of Atmospheric Dis-
persion Estimates. Publication No. AP-26(NTIS PB 191482). RTP, NC.
U.S. Environmental Protection Agency. 1972. Water Quality and Point Source
Discharge Survey of the South Fork of the Coeur d'Alene River, Sho-
shone County, Idaho, June 1977. Surveillance and Analysis Division,
Region X, Seattle, Washington, 1972.
U.S. Environmental Protection Agency. 1974. Apparent Tailing Pond Leaking,
South Fork. Surveillance and Analysis Division, Region X, Seattle,
Washington, October 11, 1974.
U.S. Environmental Protection Agency. 1975a. Bunker Hill Fugitive Dust
Modeling Report. Surveillance and Analysis Division, Air Compliance
Branch, U.S. EPA, in House Report, August 1975.
U.S. Environmental Protection Agency. 1975b. Guidelines for Enforcement and
Surveillance of Supplementary Control Systems. EPA-340/1-75-008.
September 1975.
U.S. Environmental Protection Agency. 1975c. River Basin Water Quality,
Status Report, Spokane River Basin. Region X, Seattle, Washington,
1975.
U.S. Environmental Protection Agency. 1975d. Silver Valley, Bunker Hill
Smelter Environmental Investigation. PEDCo Environmental Interim
Report. EPA Contract #68-02-134-T08. PEDCo Environmental Special-
ists, Inc., Cincinnati, Ohio, February 1975.
U.S. Environmental Protection Agency. 1976a. Guidelines for Evaluating
Supplemental Control Systems. EPA 450/2-76-003.
-------
U.S. Environmental Protection Agency. 1976b. Handbook for (Jnamap. NTIS
#PB-240-273/LL, March 1976.
U.S. Environmental Protection Agency. 1977. Office of Research and
Development. Air Quality Criteria for Lead. EPA-600/8-77-017.
WashTngton, D.C.
U.S. Environmental Protection Agency. 1978. Supplementary Guidelines for
.Lead Implementation Plans. EPA-450/2-78-038, RTP, North Carolina,
August 1978.
University of Idaho, 1974. Heavy Metal Pollution in the Coeur d'Alene
Mining District. Grant Proposal to the National Science Foundation,
Idaho Research Foundation. December 1974.
University of Idaho. 1976. Heavy Metal Pollution in the Coeur d'Alene
Mining District. Project Technical Report to National Science
Founcation, Student Originated Studies Program. Grant No. EPP
75-08500, February 25, 1976.
von Lindern, I. H. 1975a. Ambient Lead, Air Quality Information. Kellogg
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1975.
von Lindern, I. H. 1975b. Progress Report on Kellogg Lead Study, February 8,
1975. In Shoshone Lead Health Project Work Summary. Idaho Depart-
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von Lindern, I. H. 1978. Analysis of an Air Quality Model for Particulate
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Epidemiology and Environmental Health, Yale University, New Haven,
Connecticut, December 14, 1978.
von Lindern, I. H. 1980a. Cartographic Analyses of Air Quality Problems in
the Silver Valley of Northern Idaho. Progress Report to the Idaho
Dept. of Health and Welfare, January 15, 1980.
von Lindern, I. H. 1980b. An Empirical Air Quality Analysis Utilizing
Computer-Assisted Cartographic Modeling Techniques. Ph.D. Dis-
sertation, Yale University, New Haven, Connecticut, December 1980.
Walter, S. D., Yankel, A. J., and von Lindern, I. H. 1980. Age-Specific
Risk Factors for Lead Absorption in Children. Arch. Env. He.
35(l):53-58.
Wegner, G. T976. Shoshone Lead Health Project, Work Summary 1976. Idaho
Department of Health and Welfare, Boise, Idaho.
Wilson, L. 1975. Application of the Wind Erosion Equation in Air Pollution
Surveys. J. Soil. Wat. 30(5):215-219.
-------
89
Woodruff, M. P., and Siddoway, F. H. 1965.
Science Proceedings: 602-607.
A Wind Erosion Equation. Soil
Yankel, A. J., and von Lindern, I. H. 1974. Procedures Employed for Study
of Lead in Dust, Soil, and the Ambient Air. In Shoshone Lead Health
Project Work Summary. Idaho Dept. of Health and Welfare, January
1976, Boise, Idaho.
Yankel, A. J., von Lindern, I. H., and Walter, S. D. 1977. The Silver Valley
Lead Study: The Relationship between Childhood Blood Lead Levels and
Environmental Exposure. J. Air Pollut. Control -Assoc. 27:764-767.
-------
90
APPENDIX A
REFERENCED MAP DISPLAYS
Figure A: TOWNS, RIVER, VALLEY, MONITORS
Figure B: ELEVATION, ROADS, VALLEY
Figure C: ACTIVES, PTSOURCE, SPECSITE
Figure D: SOILPB, SOILCD
Figure E: DOEPASS, PEDCOPAS, PESPASS,
COVERMAP, PASSIVES
-------
91
Notes for Figure A
The first map demonstrates the map VALLEY overlaid by the river
and urban locations. The second map depicts the urban locations through
the map TOWNS. The third map superimposes the monitor locations from the
map MONITORS.
-------
Figure A
1
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-------
93
Notes for Figure B
These three maps are first the sliced version of the ELEVATION
maps. It is divided into twenty levels representing 100-foot intervals
from 2000 to 4000 feet in elevation. The next map shows the ROADS in the
valley and in the third map values above 3200 feet elevation are masked
put for VALLEY to illustrate the relative location of the roads.
-------
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-------
95
Notes for Figure C
These three maps show the locations of the Active and Point Source
locations, and the sites deserving special source considerations in the
passive analyses. The point source labels are by process unit as defined
in Chapter VIII. ACTIVES, PTSOURCE, and SPECSITE are described in Chapter VI
of the parent document.
-------
Figure C
96
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-------
97
Notes for Figure D
This series of maps illustrates a part of the development of the
soil contamination maps SOILPB and SOILCD. The first two maps are the soil
lead and cadmium estimates developed by the regression models in the report
text. They are displayed here using the "slice" technique in which the dif-
ferent levels signified at the top of the legend are displayed via corres-
ponding symbols below. The levels are in ppm metals. The third map is the
soil contamination map synthesized from the several soil studies referenced
in the report text. It shows soil lead estimates in ppm for the valley sites
(river + 200 feet).
-------
Blil I
tt
II il
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-------
99
Notes for Figures El and 12
The^irst three maps are the PASSIVE source locations from three
studies referenced in the text. The list at the bottom of the maps refers
to those in Table 2 as indicated by the variable VAL for PESPASS, (VAL - 100)
for PEDCOPAS, and (VAL - 200) for DOEPASS. The next map is the vegetation
COVERMAP that depicts the vegetation cover levels for the valley. Some of
the areas from this map are used together with the previous three maps to
produce the last in this series. That map referred to as
shows all the special passive source areas considered in the valley, and
the river.
-------
Figure E.I.
100
-------
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-------
102
APPENDIX B
NATIONAL EMISSION DATA SYSTEM
(NEDS) Designation for Bunker Hill
Company Point Sources
(Taken From PES, 1978)
-------
TABLE 1. SMELTER STACKS
Process
Units
SI
S2
S3
54
S5
56
S7
Process and/or Control
11. Crushing Plant Dryer - Scrubber
12. Crushing Plant Dust Collector - Receiving Bins
13. Crushing Plant Rod Mill Scrubber
14. Crushing Plant Conveyor - Baghouse
15. OPP Ore Preparation Plant Baghouse
16. Pelletizing Dryer - Scrubber
17. Return Sinter Storage 1 ,,n,( <.__.llKK_,.
18. Pelletizing Plant Conv. / D sc™bber
1 . Lurgi-Strong Gas )
Smelter Acid Plant >
2. Lurgi-Weak Gas )
19. Lurgl "N" Rotoclone - Sinter Discharge
20. Lurgl "B" Scrubber - Sizing Building
21. Lurgi "C" Scrubber - Sizing Building
22. Lurgi "A" Scrubber - Sinter Rolls -
Retrofit Drum
3. Lead B.last Furnace
10. Lead Blast Furnace Feed (Sinter Tunnel)
26. 21nc Fuming Plant Main Stack - Baghouse
27. Zinc Fuming Plant Granulator - Scrubber
NEDS
02
03
04
05
06
07
08
01
09
10
11
12
01
16
17
Name
Crushing
Plant
Ore Prepara-
tion Plan
Lurgi Sinter
Machine
Sizing
Building
Blast Furnace
Z1nc Funinn.
Plant
o
to
-------
SB
S9
S10
28. Lead Refinery - Dross Kettles - Scrubber
5. Reverb Norblo Flue to Main Stack
24. Reverb Granulator Scrubber
23. Reverb Speiss Discharge - Baghouse
6. Electric Furnace (Copper Dross) Norblo to
25. Electric Furnace Granulator - Scrubber
Silver Refinery Duct
7. Retort Room
8. Cupels
9. Monarchs
32
01
17
13
01
15
01
01
01
Roof Vent
Lead Refinery
and/
Reverb. Furn.
Silvery
Refinery
NEDS 01 Main Stack
Table 1. ZINC PLANT STACKS
Process
Units
Zl
Z2
Process and/or Control
32. Concentrate Dryer - S02 Monitor
33. Concentrate Silo - (No Control)
from Rail Car
After H/H Zinc Plant Acid Plant #1
Scrubber
1 ESP
After B/H Zinc Plant Acid Plant «2
#1 RSTR.-27
#2 RSTR.-28
#3 RSTR.-29
#4 RSTR.-30
#5 RSTR.-31
Scrubber & ESP
NEDS
33 (New)
19
18
18
Name
(Concentrate
Dryer)
(Zn Roasting
Plant
- 5 Roasters
-------
1
Z3
Z4
Z5
Z6
Z7
36. Roasting Department Conveyor Scrubber
34. 11 Wedge Roaster-Scrubber
39. Melting Department Oross-Baghouse
37. Roaster Dross-Baghouse
35. Residue Dryer - No Con t. -Temp.
Scrap Furnace Sky Vent
42. Scrap Furnace Scrubber - Not Hooked Up
40. 12 Melting Furnace Scrubber
41. 13 Melting Furnace Scrubber
41A. 13 Melting Furnace Vents
42A. Alloy Furnace
38. Purification Z1nc Baghouse
(Zinc Dust Prep.)
22
20
25
23
21
28
27
26
24
#5 Roaster = 3
Roasters 1-4
Each Acid Plant
3.5 Roasters
Zinc Dross
Processing
Residue Dryer
Purification
Department
Table 1. PHOSPHATE PLANT STACKS
Process
Units
P.I
P. 2
P. 3
Process and/or Control
44. Aerotec Ammonium Phosphate Reactor
43. Dryer S.F. - Anrnonlum Phosphate
45. Doyle Reactor - Phosphoric Add Reactor
NEDS
30
29
31
Name
Separate PWR
Separate PWR
Separate PWR
o
tn
-------
106
APPENDIX C
SOURCES LISTED BY SOURCE TYPES AND
SOURCE STRENGTHS
-------
107
POINT SOURCES ORDERED BY TOTAL SOURCE STRENGTH
...f EMR
• : i if <.. ", I.M sr /".3
t ' •' r-1 N "H' "'..'.IN *ri. 7
j'. ' i:v I1.:. '"-: "'i 3-3.7
IT | | f7 "\ Y' •? ^ .P
7\ n.T; "M M ST 24 .6
7;;••' r-A rj s i )r;< ??.n
(vi"-' i L 'Vi. >' i r'.i
| i I C j n '• '• v 'j i •>(• j: ° . 2
I ! IT ] A "ri'ij ""-K 7.6
r, -"LI f.Cr'U'-r 7.3
.i \ i. 11 r c. ',r ^ u ''',[" r '•. /•
M'iVf'c ^';i\ri'rR t!r
r T.-rrr T ?-'Y( ? <• .0
L IPO I C ^CPU'1 IIR .?. 1
.-. • • r t: - Y '. P ?. °
.'.''i-.n"7 "?cri:'IJL'R ?!2
f.'."frLT VI'MJ -'TR 2.0
fi't.iSliPL '.i'll L':C f 1 '. 3
•^ -T .\p FI>- MC- i .3
r ITITL 'A.r- ! v;sn i.?
•. '•' --I:F -MGim.'sn 1.1
•••LT'.'fJ r-1?\l \.V--v. 0.7
.-. ;•<(, fC n^M^'CQ C<2
L'f:C[M 'V'.YH? r.2
POINT SOURCES ORDERED BY LEAD SOURCE STRENGTH
\t. -r PPETF
CVCLTI.F •••.••ir.1 ST 'i3.r<5
I U'T-J -f: ^Tt^CL^'J 11 ,C( *>
rri(.h7 CsYTR 7.r/9R
LUCGI '.-. SC^U^PUR '+.l'iO
Ll'-G I A SC'^fir L=? 3 .
-------
POINT SOURCES ORDERED BY CADMIUM SOURCE STRENGTH
H38
NAPE
1",rLTrr '-'.MM ST
LU'-'GI N1 '•v'". IOC L^N
PC LI.ET ^YRR
Z!« rUf'F "VIN ST
:iv.c r/.rj STACK
LI:-G i P *t-n':i?E?
U^CI A J^-UJI.iFE1?
LL-GI 'r Si:*li«K*
v2!'Tl.T SCKUUPER
«2:'FLT SC^UiU-ER
CR-LTHPL RT1KMI.L
CRLf.MPL CHLLF.CT
CKfSHPL
CRrpRf.P SAGPOUSE
'TLTDP.S SCRUI5PER
cnrpnss SAGHOUSE
~r,r ' RE AC ILV.
CRUSHPL G=:Y[iR
A.MP D^YCR
rnvi.t HEAcmR
CDErR
5 . 4P1
0. 2^1
0. 3f^7
0 . 2 5 8
O.C«2
O .C76
0 .C73
0.068
,-\
o
0
0
0
0
0
0
0
0
0
^
0
0
0
.040
.C31
.022
.0?0
.014
.013
.013
.C12
.01 1
.007
.002
.rc:
• COO
.ceo
.000
0.000
PROCESS FUGITIVE SOURCES ORDERED BY LEAD SOURCE STRENGTH
F F'f '1
CCJEMK
"I. .1ST
C"T CDN r.XI- TANS
P" P:FF "h{l(lF"vF.'JT
f'J'MT, Fl.'F.
"" -;nf]F VT. KTS
TLF.C ru^
:li:TCpx PPPC
70.00
34 .00
?c).00
1 ? . (t 0
'>.7C
'•?. .CO
1 ^'0
A.' 5 <,
31
3-'«
31
3 f
*
'i ?
n
31
0
1
1
1
1
0
1
0
1
IP
1C
P
•3
2
]
C
c
0
. 30
. '.^
f\ r\
. i' *
.47
.26
, t 5
.43
.16
CO
CO
CO
40
*iO
CT
10
00
7 4
2
0
0
0
0
0
0
0
0
. 7000
.3400
.2500
.1240
.0670
.0000
.0190
.0000
.0054
ACTIVE SOURCES ORDERED BY LEAD SOURCE STRENGTH
N Jvr.
SIL !C'. SLAG P ILF
UK S-TIRAGF-
S' MKAY PILF S
SILIO'- STr^.AGF,
5. ItJ ICR STIP.AGE
^ ''Mr P S T H'-l AGF
MTJITR STO? iCE
«rn = p STORAGE
C:KE STORAGE
i n
PI'.
CDFR
PBEKR
COL MR
73
'i 5
•••}
•53
33
•ai
49
49
49
45
45
17.8
1 .6
?0.2
?1.2
2^.2
30.3
5.0
.0.4
0.4
0.4
0.6
0.6
5
4 2
T
§»
*>
i-
2
1
42
4 2
42
0
0
0
1
0
0
0
0
0
1
1
1
0
0
C.890
0.672
0.404
C.404
C.4C4
C.303
C . ? 50
C. 168
C.160
C.16P
C.OOC
C.OOO
0.000
0.016
0 . 000
0 . 000
0.000
0 . 000
0.000
0.004
0.004
0.004
0.000
0.000
-------
109
PASSIVE SOURCES ORDERED BY TOTAL SOURCE STRENGTH PER UNIT AREE
7 !'iC VE flf:i'f;
•i FT. or- SIP
TAILING POND OSB
FAILINGS APE A
L'J.'nf? YARD ST3R
!:AllG-POLfinS
FAIR GFCUnnS
LOG singer
LIMP 09 LUMfC.fi
''DVIE TIJCATPE
GRADF.C APCA FNH
""Jin FTP TH'ATRE
'".V
I I'JFOP
f R
.\irpoqj
">AP< LOT
rOUIP
S CCNC
JR
.
STDFAGE
\ WEAR JR HI
HI ASP A
J'jHU.'C POUO
TJG!E C r.fF. RPINO
HI '.'AY FILL
"Mr IF 1C CRCKN
f.'W FfJD A
f.'Vi END p
THWN SITE
TGKN SITE
STORAGE A=:EA
ATLE7 1C F If LO
L'JMTER YARD STQR
CN'E AP7S
CF ASfH PLT
E TRAILER PARK
SMV SKAI'P
V1V S'WAriP
•10THCRCSS
.
1LD
OF FREEWAY '-',27
OF «TP P/.G.E PD
IV CPAVFL PIT
W GRAVEL PIT
OF CRE GRIND
PILE
FRECXAY «23
TRAIL PARK
GYP DIKE
COKE
:. OF
\"E4
HAP
PEDCOPA
PEDCOPA
PF.DCOPA
PEDCOPA
PESPASS
OOEPASS
PCSPASS
DOF. PASS
OOtPAUS
PEDCOPA
PESPASS
PFDCOPA
fESPASS
PFSPAS3
HQliPASS
PESPASS
PEDCQPA
PEDCOPA
PESPASS
DOE PASS
OOtPASS
PF.SPASS
PEDCOPA
PESPASS
DOE PASS
DOtPASS
OOEPASS
OOEPASS
PF.DCOPA
PSUCOPA
PESPASS
DOEPASS
OOEPASS
OOEPASS
PESPASS
DOEPASS
OOEPASS
DOEPASS
PESPASS
PCSPASS
DOEPASS
PESPASS
PESPASS
DOEPASS
DOf-.PASS
PESPASS
PESPASS
I' AMD
60
<«<.
23
] 0
'» 3
13
13
12
30
24
5
36
bfi
33
38
1
20
62,
62
16
11
53
52
55
5
-------
110
PASSIVE SOURCES ORDERED BY TOTAL SOURCE STRENGTH PER UNIT AREA (COMT.)
3 PIE
MIME
:nnnn APIS
CR CUT
SPCILS
NCAR Af-'P
nKEsnt s
GYPSU* CIKC
NO Sine MCKINLEY
S OF CIA DIKE
PLO GYP POND
9IV CM N ZANKETT
GpAV.EL STORAGE
*P AREA S OF CIA
M2504 TANK CAR
7-1FL1ER STORAGE
jMHLTCR STORAGE
SINTER STORAGE
1PA.OED AREA
SRUTH CIA GYPSUM
OLD RIV CM KCC'J
r, ORAV FNH
CN'T 3 A SIN
GYP
LCT
fun
CH
FV/INT
C.IA
HI ATEA
--i.np.LE C FILL
'.IV ACCCSS MNCR
•» TiF 5H PINO
•Ul Lf.l'T N'C Lf'FLT
r,WtE'-JY Pn,\'D AREA
TF.RPACEO HILLS 10
S OF FF CF.WAY ,'J37
CIA SCUTH ROAD-
CIA SOUTH DIKE
ROSS OIL AREA
SLAG REMOVAL AR
3V CH N AIP.PCRT
N OF AIRPORT
N GYP PCHO
N GYP PONO SLAG
GP.A-VF.L PIT
RUBBLE C FILL
s OF f-r.rrt.'/.Y
-------
Ill
PASSIVE SOURCES ORDERED BY LEAD SOURCE STRENGTH PER UNIT AREA
M AP
si DRAG r. ARE
SUITES STORAGE
HO TCKN SITE
TAILINGS AREA
ZINC RESIDUE
C?A:EC AREA FMI
4REA SEAR JR HI
JR HI AREA
PARK LOT S CCKC
WMSE C
r,MV S
CRE GRIND
'F. AP7S -
TAILING PONP cso
T.MV PLAYGRdlM.D
S OF ^ rEWAY «27
'•".IT 0 CROSS
RP. AREA S OF CIA
E Oc STP PAGE PD
'•I OF CRE GRIND
LPG STORAGE
"5V IE T!JFATPE
•JHAH SHOSH ARTS
V1F.LT PAPK LOT
^ACIFIC C?:CKN
UD GYP ronn
LUMBER YARD STCR
LUJ'.l-CR
LUV.f.LR
OLD 7CUN SITE
HIU1Y FILL
ilP.PQRT AREA
MR°C»T ARCA
SWEENY POlJD AREA
r>''FLTCR STORAGE
CIA NX END A
CIA UX END B
LUMBER YARD STOR
N END CF STP
MO SICE MCKIMEY
ECU IP STOR AREA
•«U;F: SPCTLS
71CL7ER STORAGE
S OF FREEWAY ?-'23
•fSinc GYP DIKE
OUTOCCR THEATRE
DOtPASS
DHEPASS
PEDCOPA
PESPASS
PEDCOPA
PESPASS
DOEPASS
PESPASS
PESPASS
not PASS
PESPASS
DOEPASS
PESPASS
PEDCOPA
PEDCOPA
DOEPASS
DOEPASS
PESPASS
PESPASS
PESPASS
PESPASS
Pf-SPASS
not-PASS
PFiSPASS
DOLPASS
TJH PASS
PF. DCOPA
DHL PASS
PESP\SS
OQI;PASS
PEUCOPA
PESPASS
DDE PASS
PESPASS
PEDCTPA
DOEPASS
DOEPASS
PESPASS
PEOCOPA
PESPASS
PEDCOPA
PEDCOPA
PEDCQPA
DOEPASS
PESPASS
PESPASS
MAPJtL
10
60
5
20
62
19
9
21
57
23
9
27
34
40
10
61
13
12
24
63
i r
11
43
30
14
55
1 6
36
46
36
53
52
44
32
33
34
37
23
37
-------
112
PASSIVE SOURCES ORDERED BY LEAD SOURCE STRENGTH PER UNIT .".nEA (CONT..)
ipc* NEAR ZP
?IV CK N ZAf.NETT
ARC A NEAR Af'P
FAT* GROUNDS
V1FLTER STORAGE
GYPSUM CIKE
RHSS OIL AREA
S OF ClA DIKE
E TRAILER PARK
H2504 TANK CAR
CIA SCUTH ROAD
ATLET 1C FIELD
SOUTH CIA GYFSLM
OR A DEC ARF.A
SMV GRAVEL PIT
5MV GRAVEL PIT
C FILL
ACCESS PINCR
'J GYP POND
N OF AIRPORT
PURPLE C FILL
\CFTSS FRW EH
N GYP PCND
N f.YP PTN.D SLA^
?V CM N Alf-.PCRT
PESPASS
RIV
MAI'
IIP
IV CH
1F. i I L
PARK
c. n- ci
CIA SCU
N (IF ?n
•nit i ?.e
RFGRAD
GRAVEL
•:,F PINE
GFAVEL
4FST OF
STREAM
SF C PI
MII.LCUT
TH DIKE
POHO
GRAV FN'H
DEO
A R05S
STR DEO
STORAGE
CR CCNFL
PIT
ASPH PLT
BED
KECR CC'JF
NE SKF.LT
. ...F. CR CUT
TF.RRACI-.C IIILLSID
SF.DIMENT BASIN
.TAMHETTI AREA
COKE PILE
5LAG REMOVAL AR
GYPSUf PONQ
SUNSHINE POND
PESPASS
OOEPASS
PEDCCPA
PESPASS
PESPASS
PESPASS
DOE PASS
PESPASS
DOEPASS
PEDCOPA
PESPASS
PEDCOPA
PF.SPASS
DOEPASS
PEDCOPA
PESFASS
PESPASS
DOEPASS
PEDCQPA
PESPASS
DOEPASS
OOEPASS
PESPASS
DOE PISS
ME PASS
on ii PASS
nncpiss
OOF. PASS
LVICPASS
PEW COP A
PEDCOPA
PESPASS
PEDCOPA
PEDCOPA
PESPASS
PF.OCOPA
DOEPASS
PEDCOPA
DOE PASS
PEDCOPA
PF S »V. t S
DOEPASS
DOEPASS
PEDCOPA
DOE PASS
PESPASS
PESPASS
PESPASS
DOEPASS
91
25
•92
13
38
39
51
35
57
18
51
21
8
33
40
36
35
39
2
7
8
37
17
15
22
26
56
32
6
24
65
22
20
a
61
28
31
35
69
40
13
24
59
34
53
62
-------
113
PASSIVE'SOURCES ORDERED BY CADMIUM SOURCE STRENGTH PER UNIT AREA
MAP MAFIC)
«:. «-Tr.IPt:f PCS PASS 6C
* UM S COC PCS PASS 5f
: c CFF. GMND PCS PASS t>2
rrwN SHE nor. PASS t«
F '.FF/. S HF CIA PL S PASS «C
r.i'jTf= £Tpi-/.r.f: ML- PASS r,e
N OF "CPU r.r IKC PCS PASS < i
1LO GYP PDKL' DOE PASS 11
'.i/r-rj:Y pnrjc- Atcc-A PISPASS *6
',MCLT PA^K LCT T'OHPASS 12
•ILD T:XN SITE DHL PASS ^s
-in ^Esnrs P?:SPASS 57
•jn SIDE MCKIM.EY PFSPASS 3T
f.IDE GYP DIKE PCSPASS 1*7
AF:EA NEAR JK HI HC^PASS i
JP. HI AREA mill PASS 2C
MDTnC^C^S DOLPASS 3 TTL M-TA PF.SPASS t'<
•'nv;F T^E/.TRE DOE PASS 2^
LDc;^ UJMI-.ER PFSPASS M
:,i^v c'5. PCS^ASS 13
Lrir, STITAOF PCSPASS 12
n/.c ir ic CROWN PC-SPASS 1 1
••JEA3 S>-rSM APIS PCSPASS 63
i jeonuT ;.KfA PCS^ASS 't
i!RJ»nST ARFA HOEPASS 36
CIA SCUTH F:QA'D OOEPASS 57
= Oc ST" PACE PD PFSPASS 10
*A 1 3 GF:PU!JDS DOE PASS ]3
\RG4 MAR 2P PESPASS 91
M ILL I SE A ROSS PESPASS 65
AXE 4 NI/.R APP PESPASS 92
S DF cc.rEWAY «23 DOE PASS 23
E T7AILCR PA5K DPtPASS 51
tIA SCUTH HIKE Ofll^ASS ' ^L
'\ CYP PC'ID HOE-PASS 7
N CYP PnJD
-------
114
APPENDIX D
FINAL RELATIVE IMPACT ESTIMATIONS
BY QUARTER AND STATION
-------
115
Variable Designations
QKOUNT = study quarter (1 = 3/77 . . . 8 = 2/79)
STATION
1 Cataldo
2 Pinehurst
3 Smelterville
4 Silver King
5 Medical Center
6 Kellogg City Hall
7 Osburn
8 Wallace
9 Kingston
AMBPB = Observed Quarterly Lead Mean (yu/m )
3
SMLOWX = Estimated Low Smelter Contribution (yg/m )
SMMIDX = Estimated Mid Smelter Contribution (yg/m3)
ACTX = Estimated Active Source Contribution (yg/m }
PASX = Estimated Passive Source Contribution (yg/m )
TOTX = Estimated Quarterly Lead Mean (yg/m3)
FRSMLOW = Fraction of Estimated Mean Due to Low Smelter Sources
FRSMMID = Fraction of Estimated Mean Due to Mid Smelter Sources
FRACT = Fraction of Estimated Mean Due to Active Sources
FRPAS = Fraction of Estimated Mean Due to Passive Sources
-------
SUTION-1 OKflUNT-1
VAKUfLl
AC1X
PASX
10TX
Ff-S-M l
ft- ACT
FFUS
SVLfiw*
S"MC X
tCIX
F-ii»
ICI>
FF T» ITW
FF J>"H'
FI-P45
SMC.**
S"wll X
/CTX
FASX
1C1X
FFACI
FKF-AS
pfl
ACIX
%?!
fPAf.T
FfcPAS
6
6
6
6
16
f>
(.
If.
l<>
1'.
If,
It,
23
?»
23
SUNPikO
01 VI A I ION
0.47150000
0.37/0-' *?s
0.111 m:*92
C.3'/Ms874
0.
0.0lv99<,67
0. 1 l<«iOH29
0.07''.'S 9
li. J 7'-'; •••.t>77
0.. •O'j'.l/S
.7d
0. !/• 17'. »/./
0.6 n7•!••!«•»
O.'O-". J.166
0.7r
(•Ie.i|6/.<. 17
O. 0(,OvjblKIO
O.OOf'OJO«)0
C.fCOOCOOO
C.f0000000
C.003C
U.003M J^37
c.oooocooo
'J.OOOOUOOO
0. J
O.O'-'i i
0.73.U.1!
I .?/«-!'./
0.07 < 0»
/'•/
f . 2i'Hf(0;'04
(.'.»I
'-t J07/
C.C6 10 /067
L.31 Y8W15
MINIMUM
V4LUF
o.ir'.ooooo
0.25024072
0.00000000
0.00000000
0.? !
n.OCCJOOOO
H.00000000
SI AT
O.?l?00000
0.033r»3fi|',
0. I't00'17?4
O.OOf-00000
0.00000000
O.I7101 73';
0,
0,
o!
1.
1.
o!
o!
2
0.
o!
o.
i.
0. 1 V770HI9
O.OUOOOOOO
O.UOOOOOOO
STATION-1
0.10400000
o.o^.'.l)^^«.2
o.ocoooooo
0.00000000
O.OOOJOOOO
0.00000000
0.
0.
STATIOM-1
O.OHIOOOOO
0 .O/ >13l-i9
o. i no?«.(.6
0.00000000
0.00000000
0.00000000
0.00000000
0
2
2
0
0
0
O
MAXIMUM
VALUE
63800000
08654237
25900B15
125973(9
66045170
.R760B171
.2'i3915ii|
.6,781 12*>B
28200000
162177??
3?279fl?5
,6942fl?33
2/.307«.°fl
.800224."?
157697032
OKOUNT-3
.49300000
00000000
00000000
.
1755<.«>73
,00000000
,00000000
OKOUNT-<(
,33000000
1?705970
.21716173
13340-MO
.B53063
B79«.0363
0,
0
0,
0,
0,
0,
0
0
STD FRRHR
OF MEAN
07684736
00816279
04891153
04297407
17620716
21"!54587
015*3170
1177221T
Ol?|885223 ?
SUM
2.P2900000
0.35544420
2.26249947
0.66B30354
2.36049241
5.66673962
0.47551955
3.14486924
.56488058
.81473063
VARIANCE
0.03543310
0.00039979
0.01435403
O.OI10R062
O.T6629382
0.27876012
0.00150386
O.OP315099
O.OC90764B
0.08475512
0.1737B752
O.C0905525
0.03071P17
0.07649033
0.0°313548
O.C1774785
0.07226127
0.0*607483
0.07019117
11 .2P200000
?.9B407riOO
.86018109
.72687870
2.23188910
6 .80302689
1 .75420S58
7.02490934
2.13718290
2.08370218
0.3S262731
0.00106597
3.01226688
8.02IOP8H79
10.21983808
0.00000000
0.00000000
12.29589688
2.69337326
13.30662674
O.OOOCOOOO
0.00000000
S.2S264060
.00332513
0.07650535
O.OCOOOOOO
0 .00000000
0.11165639
0.0000 11>02
O.uCOOf.02
0.00000000
0.00000000
0.06021939
0.00515504
0.02279905
0.13960651
0.01I44044
0.06317255
0.01315114
0.06669170
B.72700COO
1.S0402767
7.t42fcllfi3
2.20929395
16.79278633
28.14871977
1.80098004
9.41178936
1.55591598
10.23131462
O.OB340662
0.00061140
0.01195532
0.00516386
0.3P4464B5
0.44H26947
0.00301033
0.09178773
0.00397791
0.10229900
C.V.
901
-------
STATION-! OKOUNI-5
ViMUHLF
if IX
Ff- '
F;
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76
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0 '. I »,' I • 7 »'•
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1.1
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J-; l_« '71 !'•
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O.dO'lOl
O.'IOOOOOOO
O.^C'-'./i 714
o.cooooooo
0.00000000
Sf4T
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0.07-iI I 708
0. 1 If.)* *>64
O.uOOOOOOO
0.0000001)0
0. 1 <.M /;• 72
0.00000000
0.00000000
H4XIHUM
V»LUF
O.II?Vi770
.3*7»»
0. H3052I 1
0.56659393
1.61466625
0.392703P5
2.
6.670I>)
0.70P1|037
0.7."S69776
0.3039977?
0.
O.H0032960
O.fll59262l 313
0.01398021
0.0*742035
SUM
5.42997959
I.71B20955
7.89571*67
2.84496781
10.70568439
23.16458042
2.50530713
12.46P879B4
2.81061465
8.1301983d
15.39636735
1.86953405
17.34701019
0.76204078
0.94369419
15.91727971
3.4C604796
23.01902979
0.75857441
0.77634784
16.277EI633
3.91474PS3
?I7BI30'»96
.430)1)49
.34266111
3.56770H72
21.07847042
0.55970J43
1.84411743
5.55810204
2.47568679
9.0566/174
7.61309063
15.346R2797
29.49227663
2.764H8509
10.69353002
1.96784171
10.553)4237
VARIANCE
0.03194056
0.00026373
0.1)0*23411
0,00784832
0.1FVI9S36
0.7^986291
0^01765608
0.0H41443
O.OW2S753
0.4 Mf'6776
O.CCIH70V8
0.0015244
8.00735H6H
.01 5*^ 7b80
0.12710443
O.OOO796B6
0.0/047932
O.OOA1')925
O.UI036568
0.3S950H91
oIo7f»7Si03
Oll«744)82
1.55031847
O.OOI36254
0^00*19949
0.03)26)21
0.01501227
O.O01379B3
0.01470*73
O.OO647936
0./C263H42
O.?i»l45232
0*0ht>34099
O.OO*>OA160
0.06572451
C.V.
65.037
l\:W
80.963
105. 8O4
57.216
50.055
58.108
103.071
91.663
119.485
64.763
57.012
315.200
375.058
62.715
23.2O6
17.377
334.231
367.198
104.841
93.546
29.322
246.762
302.202
122.951
27.935
24.656
272.864
267.044
57.315
39.011
34.814
80.091
76.263
46.770
55. 148
62.624
94.185
72.131
-------
SUTlflN-2 OKOUHT-1
VAK |f. I'll
tw P r>
I""! CX
ACIX
MASX
T.TIX
Ft SM LOw
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i. .CO'100000
L.Oil.tOOO<«2
/.*4?<--?89
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0. •* ^ 7 *• ''01 6
c.i-OOOCOOO
C.I 4 12 *lv!0
C. /«)71 .317
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i:.<3». 7 7J-/'»
0..'2? »o(,H2
C. 71-. »/'•>»» 10
C . OOliOUOOO
O.dOOOLT.OO
0 . •» / "1 ' . >91
u.P I'.s .iu.O
i .11 IS''. • '
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<• . <* /7''61 »4
l<. 73'jr.-1/0;<
C. 0 '•r>62:'l
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0. IS44 .'466
VALOf
0.93700000
ol3H7 l?'»4?
O.OCCOOOOO
o.oooor.ooo
0. 1 ?7?f-^'73
O . tif/2t> 7"H
0.00000000
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0. 37400000
C. 0770?' 70
0.31^7:"".f>
o.ocoorooo
U.COCOOPPO
ol l 3lur.t74
0. SO? 1?(.? 3
0.00000000
0.00000000
0. 13JOCPOO
C1 . 1 '54 ^ 1 1 1 -0
0.* 601?" 15
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0.00000000
0 . 7''r.0'«^lf-
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0^04 7'<"Q IS
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0.00000000
0.^06 I4«:94
O.OM1 JVC
0.1101 ftfl'fO
o.oooocooo
0.00000000
'VALUF
4.77300000
0.65661P13
.oloooooooo
3*.3S011002
0.21552731
O.H722I127
0.00000000
0.202174PI
ION"2 QKOUNT"2 •
10 .60400000
0. 65322385
1 ,4*'7367'> 7
o.o-ioboooo
2.*40526il9
0.2715HI03
0.803911 10
O.OPOOOOOO
0.36578S03
I ON* 2 QKOUNT*3 •
7. 11600000
0. 94667907
6 '.00000000
0.00000000
4 . 36648049
0 .21 680^^8
0 .M 30 JOP48
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0.00000000
l riM • ? rt^ n i ifj T • /.
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?. 11600000
0 .Pi^655??3
1 • 6°404 7H 3
O.ilOOOOOOO
o.ifts^mH
3.1 44 36008
0 .441 8 6^ 2 3
0 . R1H04908
0.00000000
0.62869129
STP F.RWOR
OF MEAN
0.64968559
0.11699289
olcooooooo
0.10087624
0.51673684
O.P1793566
0.0«'?14708
O.POOOOOOO
0.03711892
0. 09*86253
0 1 0601108 3
O!CPOOOOOO
0.04531259
0.22363412
0*033«»9188
O.COOOOOOO
0.03769715
O.(.74«,l?20
0.06702^03
olopnoodoo
O.PPOOOOOO
0.. "0365726
0.00470316
0.00470316
Og>/\t\nn/\ t\n
. UiHMIOOUU
0.00000000
0.12K-77S5
O.P<-«
-------
SUIIDN-? QKOUNT-5
VAUUbLC
icrx
FASX
tni>
^^ swt nw
AC1X
P/.SX
101X
FIIC.I
FfcPAS
jMi-pn
Tf.TX
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30
30
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30
30
30
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O.h". 7 • J52H
O.'O? »7M9
0.000JOOOO
0..'J4'. 7 II.)
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0.7s ••'. jl 7i-
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clocoocooo
( .I'OOoOOOO
r. II /12974
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5
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1.0. • I »'.07
I'. I 77(.-.H/2
0. '• I «'i'.-»s*.
o.(.(i'iri )H'lO
O.C(i.«i 7' tl
;.. 7 s| • •.'. 2
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( J< COOuUOO
c!l'--*»464? 7
liO.V. *'
f./7l IsM
...'.'•• ?." U
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77
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I IOOOOCOOO
C. 1 70 141J 3
HI Ml MUM
VALUF
0.
0.702*00)0
P. 00030000
0.00000000
0.0"H|?V'>9
0 I 00000000
O.OOC'JCOOO
SIAT
0.0' 3MI(. 33
o.ouooonoo
O.OOOOOCOO
0.1011
0. 17^9 V O9
O.OOOOOOOO
0.00000000
STAf
1.0-:?
r.^^777-64
(I. (lOOOl/dUO
O.OOOOOOOO
n'.yiT) i<,|<.
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0.00000000
0.0h5',l .^73
(I. 37'-')l-!<63
l>..10000000
O.OC000100
0.01 7MM4
0.00000000
0.00000000
MAXIMUM
VALUE
5.H0571479
2,
0.
0
0.
0.00000000
0.74663440
,78540232
.6771 1061
.83147234
.00000000
0.412I6HA6
o.ooonnono
0.62406^91
0.00000000
0.25377451
11 .
II ."'.1.573740
0 .01000000
7ll2'i?2 72P«
0.49PI0777
0.01POOOOO
O.I 7002001
STATION.
J.on749M
(I ! 00 00 0000
J.M«>40676 7
0.80I0323B
0.00000000
0.67433801
SIP ERROR
OF HE AN
0.70244426
0.05799377
0.077I10«»3
0.00000000
0.03783293
0.11864215
Ol6?6l0060
O.POOOOOOO
0.0213P487
0.*8S34045
0.0"64|385
0.23660181
O.CCOOOOOO
O.G1M0524
0.2°716543
0.0?2<-0325
0.07378084
0.00000000
0.01219971
0 '-8051418
Ol497i?977
O.ro?74014
0.00000000
8.0175601)
,9R«i00957
0.01636579
0.017*7053
0.00000000
O.C0747309
0.177761 12
oIlOOo'f.766
0 . 00000000
0.0«-55HH66
0.16209492
0.01610792
0.07952674
0.00000000
0.03336744
SUM
24. 00539776
12.98628573
28.86871491
0.00000000
7.00420883
48.85920947
7.48511.135
18.71694429
O.OOOCOCOO
3.79794237
70.35887755
9.641454H4
33.* 7550214
0.00000000
0.61995035
43.93690734
4.7C04A366
19.85017363
O.OOOOOCOO
0.44934272
54.87742-157
20.628303<<9
49.9?6664HB
O.OCOOOCOO
0.4H283825
71 .04780662
4.06607008
18.70979994
0.00000000
0.20412S9S
n .52355102
.ti3H47f?5
.64710675
O.OOOOOCOO
9i616t<2637
49.09740937
5.9179)568
15.18642642
O.OOOOOCOO
4.89563790
VARIANCE
1.14754298
8.IC089H30
.17838788
0.00000000
0.04293V92
0.42227681
0.01852952
0.03909759
O.OOOOOCOO
0.01371938
8.0*558613
0.23739075
I . tV»51039
0 .UOOOOOOO
O.OC6*«7t62
2.2C768229
0.01254764
0.01413* 20
0. UOOOOOOO
0. 00372062
7.7M73076
5.70J4B333
olococoodo
O.C0709256
22 . Jl*>60b6l
0.00016030
8.00730414
.UOOOOOOO
0.00176448
Ol2<035197
0.00000000
0.0f-03421>9
0.61*314387
0.00674609
0.02766676
0.00000000
0.0 2894 bO*.
C.V
124.94*
73.381
43.89«
w
88.75!
39.901
54.551
31.69:
^
92.52:
103.99
124.99-
87.82-
336^821
84.54:
59.57
14.97!
339137
116.791
266.25-
110.93'
401 1 1 61
I52.92i
44.1§l
10.501
403181
77.60:
63.75i
47.99'
76^63
43.76'
it:??;
90*35'
-------
VARIAPIF
AC IX
intx
i«. 5^-10
FPAtI
FKPAS
ACU
Ft*J>
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VALUI-
1 . 7sriOOPPO
P. lusj /.--TB
O.OCOOOOOO
o.ooooocoo
0.00000000
SUTIOS-3 OKDUNT-l
MAXIMUM
V4LUF
15.63?00000
0.10?'.9I70
o.onoooroo
o.oooocooo
O.OOCOOOOO
6.300')|S71
0.9 11 196 10
0 .0305S900
0.21*66938
SWIOS-3 OKOtNT-2
0. '•••«>0f'0ro
uloooouooo
O.OO'iOOOOO .
O.HOOOOOPO
0.00000000
•- SIA1
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or233-M««I-M
o.oooocooo
0. 00000(100
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0. ^2«>')OPOO
0.11* 1.1710
O.OCOOOOOO
0.00'OCOOO
O.OOOOOPOO
O.rtl^l* »73
0.l03'3"?-»9
0.00000000
0.00000000
O.OCOOOOOO
2f>.*9(tOOOPO
11 .7*3
1./.1P
17.3 V»*S76
1.00000000
8.75300336
.I*162f.l8
30.85500000
6.39100!«74 7*>e'4B
0 .04 1 P ^ 9 50
0. P* 1 85950
0.00000000
0.00000000
0.*M 16746
0.1 ?** 9fl ^88
0 00**P*2Bfl
0.07720923
o"o*Hs0336
0.*004H3373
0.03083572
SUM
41 .46ACOOOO
13.31352'H*
OI*OH77131
2.04321533
20.35565624
3.31802H6I
2.0*518592
0.09421066
0.54257482
137.10100000
44.35026^13
14.2B393679
1 .14585009
1 .45626277
61 .73631H79
P .104976HO
Ol351lii«44
0.46H6222S
115.89500000
29.5S973I17
33.52704544
O.OOOCOCOO
O.OOOOCCOO
63.12677661
6*1 3^0 7 1 f> C
8.86192MSQ
O.OOOCOCOO
0.00000000
84 .66300000
3«.3?550s«J«
25.58182U52
1 .0068H236
9.32887770
70.24300556
10.37667143
•10.96716390
0.3839821)3
3.27218185
VARIANCE
23.39636*67
3.99602912
1.05108651
0.00496717
0.10*367.80
3.51*13567
0.09262560
0.1*6*7*51
0. 000177??
O.OOP8P728
13^2373170
0.2/276577
O.OI6H9851
0.01*73234
13.2*08*912
0.0749P015
0.07*09719
0.00|s9f-H6
O.U0322<<93
65.5932*352
3.0*542261
1 .lt-050276
0.00000000
O.OCOOOOOO
6.P 2Vfi?670
0 0 ** 1 7 P 1 ? 6
0.0/62P.126
O.OOCOOOOO
0.00000000
1 1797*2960
0 3 "6 79^ ^^
0.002*2206
0.1*90316*
1.81779767
0.0?"i57214
0.0f.h03757
0.0005P412
0.02377104
C.V.
69.9P6
90.069
134. Q12
103.449
96.669
55.255
S5.03S
112.318
84.794
104. 250
ill 'I 33
57*419
181.517
150.375
54*056
63.376
176.421
165.955
104.823
95.420
48.610
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,
62.098
§9.619
7.*41
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68.118
97.645
61. 559
1 22. 1 95
!47l985
58*308
53.962
157.355
117.795
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0 . 4 « 75 -s IH 4
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SIAUON-3 OKOUNT-5
HAX IHIJH
VALUF
•».Ti8l63?7
i167474206
0^92518665
0.727IO-S35
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1.22007^24
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15.95H36735
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0 . 2B.I-.«Sf10
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7. 9055215ft
0.94346739
0.69603546
0.14102062
0.40321740
STP FRROR
(If KEAN
0.372604NB
0.2640921B
0.111472^7
0.020^^346
O.C«>451«44
0.21075650
0.0"273l«9
0.0P4<.0!10
0.1*>0«»0724
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0.0?lP*°fS
0.7*«74
19.JP724133
0.1356773C
0.95391504
92.35747032
I7.79979rtl6
9.B02667H5
0.04250H<,0
0.35502559
129.12432653
Bl .fl 7950699
34.4P3C559J
0.9«-372271
1.51053439
I1H.U5682005
15.04193350
12.33699090
0.24279253
0.37628307
66.6C67755I
60.6I721I27
9.7ll4BOe7
3.32952V17
6.97069695
80.6289|a26
17.27858313
5.16989397
1.02811980
2.52340310
VARIANCE
3. 74*52874
1 .95265096
3.34793423
0.01217627
O.OM322329
1 .7f 060697
0.077fl5H27
0.07V93272
0.00124291
0.05181*51
2^.4<'^37252
10.:»M04932
o!u'co23e»4
0.011S0072
B. •>0*>68 139
0.1
-------
STATIONS OKOUNT-1
VABUPlt
APPFfl
AC1X
TCfx
M'S—lT
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FPPAS
WfPP
5^l_rV- X
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7. 1702041 1
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0.06 /1C.S6
oI5l7«»-.7' i'.^
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4 .71430000
0.00000000
0.00000000
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0.00000000
0. 33320«:93
0.00000000
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0.00000000
o.ccoooooo
0.9hHOCOOO
0.2 7** 1 ^^70
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0.00000000
1 .05300000
0. 70406 163
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0.00000000
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0.00000000
0.00000300
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0.00000000
0.00000000
0.40V Jl.« 10
0.00030OOO
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0.00000000
0.00000000
MAXIMUM
VALUE
23.22400000
11 .63875890
0.174J920B
0.14765*02
1.54419416
12.1075B074
1 .00000000
0.52337001
0.04300994
0.95699006
10N»4 OKOUNT-2 -
26.99700000
Ol4a67?<»70
0.46477040
2nl71t5?6l2
1 .00000000
0.49976473
0.15423560
0.317BOP46
lflN«4 OKOUNT*3 ~
30.27100000
17.7497fl503
0.695J41IS3
0.00000000
0.00000000
17.74«i7HS03
1 .00000000
oloooooooo
0.00000000
10S4»4 QKQUNT'4 —
1B.9470COOO
9.705H916B
0 • $ 1 3*1 fl6 19
0.1607.7079
?.0??69705
1 1 .7990 761 3
1 .00000000
0.16915.1*1
0.37*60471
0.96667050
SIP FRROR
OF MEAN
2.92722357
1.7357B627
0.02906535
0.07456208
0.7400P416
1.71M27427
0.153PB65B
0. OP 7 228 34
0.00737520
0.15006018
2.2140B077
oIP3r>57P21
0.01473311
0.0*137953
?. 1*2 77119
0.0'«263759
0.03U20365
0.0)170124
0.01993654
2.fr-563035
1 .4M67271
0.064412«9
8.00000000
.00000000
1.47-90667
0.013019B5
0.01301<»fl5
0.00000000
0.00000000
O.P4145B47
0.4<>|I0649
0.014409B3
0.00-JF400B
0.1?«91261
0.572531-54
0.0717P547
O.C1067997
0.01170463
0.06564461
SUM
74.92300000
22.23498411
0.1743920H
0.39840477
3.53947009
26.34725105
4.02198186
0.52337001
0.10322728
1.35142082
197.53900000
109.93686608
1.4696UJ67
1.44925290
2.01B02H36
114.69383*00
13.47006547
1.59507600
0.402204HS
0.53265364
174.21BCOOOO
82.37039058
3.01652538
O.OOOOOCOO
0.00000000
85.3P691596
13.52959658
1.47040342
O.OOOOCOOO
0.00000000
162.06600000
60.37661154
0.64774d22
1.25391199
15.53834648
7P.OI661H22
1 7.07156')46
0.70736U31
0.93265H39
7.26840384
VARIANCE
51.41182697
18.07772377
0.00506677
0.00361977
0.34584242
17.71479H84
0. 1M)9P066
0.04565270
0.00032636
0.13510e34
7B.43445f30
75.J41B1736
8.07025294
.01930300
0.04273770
74.rt412l«|46
0.02908743
0.07335230
0.00219070
0.00635945
97.96B6969B
32.17H99226
0. Of 277396
O.COOOOOOO
0.00000000
30.(.76613BB
0.01635466
0.01635466
0.00000000
0.00000000
16.40936)10
6.270h2l>72
0.00*39*73
0.00751751
0.4 ^HflJoBO
7.0S901957
0.1«-I55170
0.00796560
0.004H8324
0.11203960
c.v.
901606
99.690
95.848
56.013
244.949
105.004
163.193
71.733
126.327
152.851
153.367
iK-2tt
120.4.7<»
20.256
153.266
166.193
239.544
65.220
103.301
124.090
97.219
14.178
130.459
•
68.634
107.837
225.347
104.038
110.843
88. 794
57.300
200.163
194. B07
119.406
t\J
-------
VARItfclF
S-tOHX
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0.00000000
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0. »<.<.XV\G
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(.'.00(1(10030
S1ATION-<. OKOUN1-5
MM IMU1
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10.r»3S<»5020
1 .5706^133
1 .00000000
O.I62'»<>lt>7
0 .1
0.77'
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0.00000000
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13.«>t.7346«»4
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25 .9032*336
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0.76P93716
25 .90374336
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11.00000000
0.00000000
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17.76773621
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0.00000000
0.916903P2
1«.H5«.523«2
1 .00000000
0.2?163721
0.72601069
0.52832878
STP CKROK
OF MEAN
0.39R00720
0.0125496)
O.OIH5156I
0.100I6C.53
0.07^53270
0.007MS46
0.06989169
SUM
100.66746939
80.39615567
0.52525547
2.06025574
16.77213269
99.75379951
1B.1C237121
0.366D1J43
0.9299I7H1
C. 60089 755
VARIANCE
4.43547424
B. 15026532
0.00440979
0.00959918
0.2P093332
6.3(607321
0.1^974^27
O.OOI5M50
0.00222460
0.13677576
0.7992B301
Olo340300n
O.PP/.17«47
1.22R56I42
O.d371>7662
0.0030fiS2n
O.OI77739A
I43.B930204I
95.4769H032
2.36714619
0.2H1B94HO
1 .4704BP7S
99.600M009
17.96691413
2.3732H1 22685
0. 0^323724
O.Of>M64l»l
0.03344A07
1.10112601
0.0254S55H
0. 02*51M4
O.OP720224
0.00375964
144.2IH55I02
151. 934737s!
2.B3483496
3. (.20 10641
1.77511 751
160.16484628
23.68326150
1.7625H'is5
0.37365H04
0.16049421
23 .H2491t96
33.34172182
0.03093199
0.11171422
O.OJ132566
33.94939793
0.01604765
8.0»b«2154
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O.OC036751
8.«>1992940
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O.C9I 17005
0.062K4516
0.9-»371B87
0.06913092
0.010367T7
0.04194446
0.03193822
9
13
. 7J1 32653
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365II05B
576159
809773
3B4092
242423
0.46103030
4.0 84)7033
3.28237515
.
15.015
11.398
159.523
IB. 172
4.H7034641
2.U0302
8.00IB
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O.IO26B736
2272
1731
1I44
B736
23. 649071 «4
6.12425618
0.00279477
0.0^574278
0.0^652130
C.V.
58.567
99.428
353.995
133.154
88.485
61.168
61.821
303.562
142.017
120.398
65.310
.86.507
344.021
163.529
20.617
152.369
?90.669
306.721
P7
-.51
279. lib
101.861
14.250
191. b66
255.536
276.148
1
i;
3.097
9.260
.0.434
'8.137
6.154
8.998
-------
STAllin-5 OKOUNT-1
VAR lil'LC
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0.71000000
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0.
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0,
M
74l/<>'.l-7
0 1.>I»«S4«»7
0000OOOO
OOOUO.'iOO
27.<1'.''44
MAX
VALUE
14.72300000
4,
2,
0,
0.30B71745
6.
O.I
0.'
0.(
0.!
•5 QKOUNT-2
22.12100000
B.4J971765
7.61*012*9
o.r-
o.;
11.004
0. 7292997
'1 OKOUNI-3
16.443POOPO
CCdOPPPO
00000000
STATION'
. 2 J4OOOOO
O
o.oooonooo
o.oroor,ooo
o.oooooooo
7.'
0 .OOO'lOOOO
0 .0^000000
11.'
o.:
O.I
0.00000000
0.00000000
•5 QKntNT«<
1 .90800000
C.1P1477J9
0.00000000
O.IIOOUOOOO
O.OCOOOOOO
2.5071*772
0.1104310B
0.
o-91
0,
0.09477116
0.
SIO ERROR
PF MEAN
1.9PB97717
0.">7»4B01
0.31377300
0.07466477
0.047^5738
0.7P7724B7
0.0"331M3
0. JP441724
O.CP6 71640
O.M7«2229
1 411-I742B
OIM696549
0.70242324
0.0?4',4421
0.07103430
0. 777*8900
0.04771246
0 • 0^ 7A't{t?0
O.P14«M234
0.01376075
1.2<1048 1 9
0.14117196
0.00020639
0.0770/S644
0.77190446
0.04474498
0 • O't S 55ft fl^»
0.004 73878
0.01935316
SUM
38.P2400000
fll39B99175
0.294B1602
0.71635431
17.25877073
2.21039219
3.467*3306
. 0123607319
99.316COOOO
21.76603120
21 .406454CO
O.fle53e'>76
0.7739H151
44. H 316604 1
4.7617*}0(>2
B. 563604 J|
0.37965750
93.84900000
1». 01100169
44.4310B209
O.OOOCOUOO
O.OOOOOCOC
67 .4420MJ7B
3.B4466H65
11.15533135
O.OCOCOOOO
O.OOOOOCOC
59.98100COO
25.32718664
31.4J963439
0.83H12200
3.5210040B
61.10634711
7.976S01H5
15.02476170
0.35499491
1.69333354
VARIANCE
23.73606187
?. 14453346
0.74HB10H7
0.00365011
0.01345633
3.7)8337<»4
0.0^729220
O.Ct1*67B9
0.00027389
O.GC190*B1
2B. 707704*9
Ol'".7165737
0.00« 36527
0. 00*7740?
Olo3l9?417
0.03204962
O.OOJI1329
0.00265102
23.41490169
1..-4B12I 14
3 .4?M«»»534
O.OOPOOOOO
0.00000000
B.tf>Vi31 30
0.00'.?0995
0.00420995
0.00000000
0.00000000
7 .67220325
1.1.3385981
0.4Sh23P06
0.00211b94
0 .OIH314H1
1.S030P175
0.0*.0052P.4
0 •Ot>lH6**()5
0. 00016 140
0.00936362
C.V.
75.293
111.951
61.817
122.957
97.154
67.037
62.073
44.270
115.865
110.954
75.498
148.462
49.534
144.622
169.432
90.856
52.531
29.267
205.753
244.393
77.407
93.054
62.607
*
m
70.730
25.315
8.725
•
68.129
122.247
56.164
137.307
96.0B9
56.439
70.559
37.901
166.861
42.863
-------
STATIQN-7 (MOUNT
VAKI
AMJ-P9
Id*
FiS»
TOIX
ft s^ir
ffS--I
FIUC1
FKf'AS
tCTX
TOT X
FUCI
r.-tnwx
ACIX
P*$X
TCT*
FKtCI
*CTX
f>S"«-ll-
FFiCT
FRPAS
77
77
77
77
77
77
<7
'n
77
77
77
77
7r.
?t
7*-
/I
77
77
77
; 7
•1EAN
SIANOAtP
Til VU1ION
O.Cf-,^704,7
0. V7'i'.? »•>
O.liO'l >T»M
0. I7i|I 7M l
C. 7J> VI 1 •
C.P321I70J2
cl
0.06V9 !'.J9
1.1- M7--I7
<.•'.' '-W.»fc4 )
O.CU>' '.<.'i4
C.Ctil-I*. » V«
0.<
0.
C.2 »1J'03
i . C-0|;4<59J6
0. --.I93V403
(..07902863
•< 71
I . 'ti »
C. 1^ I
C.I7 i
O.I.PV
".<. 7
in
7.1
0..'
O.S . .
C. I vi ).»9 \i.
O.»".'. /!'. /'.
J.I. U'«/. «" <•!
O.CO ' I / I'--.
!.«'. 711.IJ48
«Ti>i*> ft
f . 0 30 I t
^ 7
t.OI' '»iu Vt
. .
0.1-' I/I -I
,1. 71. /T. I-A
C. l/t >' 676
C .i»lt'-0rt»j6
I.01I/D7?)
f.JPSZrPI
C.O.'B«.H9flr>
MINIMUM
VALUF
MAXIMUM
VALUF
1.36914
ooooonno
COOOOPOO
o,
o.
0,
0.12413770
28
7?
.OOOOUOOO
,00000000
0.52810146
0.069279.-0
0.10476S9?
P.6532IA61
84033149
11131472
22990914
STATION-? QKOUNT-6
0. 1« ""JHSSO
O.OC000003
0.00000000
0.
0.00000000
0.00000000
STATION-
O.OSO"C077
olocobcooo
o.oopocnoo
pj 1 IH'^799
'•.oo(.oo;>oo
o. (10000:100
si At irv
O.OOQOOOOO
0..1000GOOO
O.I- 79707M
0.00000000
O.COUOOOUO
0.1?.n70<>?7
0.2931*613
0.01197730
3.04594124
B.39346939
419249P674
O.P5474181
6.04«i'5?970
0.10536771
0^06742489
O.I9)9I9<,6
0.9711
O.AI06002A
0.0^674^14
0.11148412
STO ERROR
OF MEAN
8. 06393671
.00618156
0.01691302
0.00787400
O.P0569073
0. 0773491 3
S .008640 70
.01804992
0.0052*a87
0.01345907
0. 72417«49
0.01244856
0.0*45?7A5
O.CP037772
O.C01C5835
O.C65 30871
0.0055fl656
0. 00*697 78
O.P0059782
0.00168671
0.43M0252
0.0.1R57991
0.1*435657
0. 0023877ft
0.0046)269
0.70245676
0.00259697
0.00772B27
0^00573992
8.0*341579
.00449711
0.01609573
0 .00306169
0.00310682
0.07303237
0.0078H48
O.OP7I6053
0.004A6433
0.00548287
SUM
11.97526531
2.1(445263
». 84418447
0.24569117
O.P71340H2
12.14606909
19179966962
0.49779398
1.89335487
31.5I900COO
7.46402636
15.234061.70
0.01470298
0.02857553
17.74136566
3.56560602
23.36551921 x
0.07327353
0.04554124
54.30615306
3.96HQCHI1
21.79094)23
0.124154*8
0.20794578
26.091t!560l
3.91B02715
23.66046930
0.15VC4V90
0.26245909
11.40216377
2.76440623
10.811)4444
0.3V8531V2
0.1f>5307M
14.15939039
5.2655HH83
20.710564H5
0.69B5ls«,fl
0.32533183
VARIANCE
0.11037339
0.00103171
0.00777336
0.00072307
O.OCOh743B
0.01340606
0.00701987
0. OCa 796 99
0.00075525
0.004H9096
1.3 ".692 3 93
O.U04I44IO
O.Of-0771'74
0.000003f<473
0.0001PHH4
8.00167733
.00074^37
O.OCOV7251
0.07703765
0.000 (.67 13
0.00779908
0.0002*343
O.OC076061
0.01546446
O.OC071342
0.0013f>436
0.00063nt7
0.00081167
c.v.
74.905
261829
164.113
91.627
25.815
25.207
12.790
149.059
99.731
99. 786
70.879
50.217
360.433
919.615
51.645
21.961
3.418
360.371
519.615
116.981
144.025
111.750
264.293
326.656
114.965
2.821
.639
278.001
324.027
fc5*?2!
107**852
2S5*21 7
231713
7.491
4.851
,97.700
296.449
VO
-------
STATION'S OKOUHT-1
ACT*
F/SX
TPIX
ft- <""•!("
ff iCI
FtPiS
6
6
6
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C.-'-JO-
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1.01226919
0.00000000
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0. T7H6M90
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0.17133110
0.85667119
o.ooooonoo
0.00000000
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0.11700000
0.0174'
C.00000000
0.00000000
O.K. 7-« .\? \ 0
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0.11 IOV299
o.oococooo
0.00000000
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0.0611 494
0.0?0<.6140
0.00022109
0.00000000
0.00443153
O.lr449t20
0.01036-iOl
0.Of 101933
O.OCOOOOOO
0.00000000
O.OM37r-59
0.00067S42
O.COO67f.42
0.00000000
0.00000000
14.10200000
1.451R9210
7.27036416
O.OOOOOCOO
O.OOOOOCOO
12.5056715B
O.OOOOCOOO
O.OOOOOCOO
O.OOK.1150
0.03904458
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0.00000000
0.0C.650445
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0.00000000
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01367610
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0.0610352B
11 .B73COCOO
1 .04376<>20
5.5000 1S<.S
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5.2634>*Mlfl
11.80729786
2.B94J5^>07
15.34652213
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6.75912280
0.074S<.?41
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0 .00467*^90
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0.0r.r>96731
0. IOOB5195
0.00235215
0.0(63161 3
0.00000000
0.09313265
124.84
60.63
58.20
165^66
54.64
40.67
43.20
144196
63.64
41.47
40.76
40.87
1.57
0.31
§7.60
3.42
31.06
14
6
3
24
89
41.95
471
67.2
41.8
co
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-------
SUTIdN.lO QKOUNT-3
VARUt
in:
FKACI
FhPAS
fl Dm
ici» '
F/.SX
11 II
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0.2
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O.OOOJOOOO
d 1747 JS13
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0.00000000
0.00000000
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O.CGOODOOO
U.00713140
0.00733140
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0.00000000
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0.0(. 103000
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0.' I 7 7t™ 17
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C.00000000
0.00000000
C.34410760
C.0401J133
C.04(l*> U33
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0.00000000
tliJ'.t>.
0.00000001
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0.
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f. .
C. 097 17V. 7
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00000000
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7/563377
090S 71 37
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1 .11" 'tooo
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0. I/'O1'
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0. J' »/-»'iVft3
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0.00000000
5.99317755
0 . 7 714 11 I 0
1.97I47MR
O.OPOOOPPO
O.OOOOOPPP
0. I l>>6<-460
(OlCOOOOOO
n.ooPonooo
J9«i40
0.00000000
O.OdOOOOOO
STD FRROR
OF1 HE AN
0.1251S012
8 .03325143
. 14546907
O.CPOOOOOO
O.OOOOOOC
..00189296
0.00000000
0.00000000
sun
9.78900000
3.51881034
16.47670760
0.00000000
0.00000000
19.99559802
2.61352998
12.38647002
0.00000000
0.00000000
0.0
65B486
74187t
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8:47*4 3 568
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0.000053
0.000093
0.000000
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0.00810621
0.0081062]
8.0000000(
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153575
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14.34135456
4.442404;
20.557
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8.00OC
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0.0415666
O.OOH4073
0.0<.85J4A
0.0000000
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1841004
0.00164279
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0.OOOOOOOO
0.03662269
0.01H40301
0.040«6708
0.00000000
0.00000000
0.05208989
0.0171)770
0.01711770
0.00000000
0.00000000
6.03185714
5.J3B74219
17.43747646
O.OOOOOCOO
O.OOOOOCOO
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0.00000000
0.03755471
8.00948278
.04607926
8.00000000
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0.07597398
8.00870S44
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0.OOOOOOOO
0.OOOOOOOO
0.034P2394
0.1^5t>5013
O.OOOOOPOO
O.COOOPOOO
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0.00000000
30.21117143
5.05674997
26.05299/06
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31 .[0974703
4.07124744
22.92875256
0.00000000
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0.00000000
O.N4US573
§.00216495
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------- |