TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1-REfpRZ-N6°00/7-80-l58
2.
3. RECIPIENT'S ACCESSIOf*NO.
4. TITLE AND SUBTITLE
Fugitive Dust From Western Surface Coal Mines
5. REPORT. DAT
'Intuit 1980 ,.|ssuiag Date
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
Frank Cook, Arlo Hendrikson, L. Daniel Maxim and
Paul R. Saunders
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Mathtcsch Division
Mathematica, Inc.
Princeton, NJ 08540
10. PROGRAM ELEMENT NO.
EHE623
11. CONTRACT/GRANT NO.
Contact No. 68-03-2477
12. SPONSORING AGENCY NAME AND ADDRESS
Energy Pollution Control Division
Industrial Environmental Research Laboratory
U.S. EPA
Cincinnati. Ohio 45268
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
Field measurement of fugitive dust levels were made 250 to 500 meters '•
downwind of mining activities and areas at four surface coal mines in the
Northern Great Plains during three different climatic conditions. Ambient ;
dust levels were also monito*red. Wide ranges of temperature, wind speed,
wind direction, precipitation, soil moisture, and mining activity levels
are represented in t?he field data.
Statistical data analyses have been extensively employed to examine
trends, test hypotheses and explore relationships. Key findings are as
follows. Mine-to-mine differences in average total suspended particulates
(TSP) levels were significant; the evidence for seasonal differences is
weaker but consistant with physical theory and prior judgements. There
are indications that snow cover, in particular, is associated with lower
TSP values. On the average, downwind TSP levels were 35 percent higher
than ambient (upwind) levels, although ambient levels exceeded downwind
levels during 29 percent of the sampling periods. Most mining activity
levels, except pickup truck operation, were positively correlated with TSP
lesvels, but only the following correlations were significant: dragline activity,
haulage, on- and off-mine traffic^ ,Overall, wxnd speed raised to the
coal haulage, oh- and off-mine traffic.
0.4 power was the best predictor of TSP levels.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b.lDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
coal mines
dust control
air pollution
coal dust
Western United States
pollution control
dust
13B
13. DISTRIBUTION STATEMENT
Release unlimited
Available from NTIS
19. SECURITY CLASS (This Report)
Unclassified
21. NO. OF PAGES
255
20. SECURITY CLASS (Thispage)
Unclassified
22. PRICE
EPA Form :!220-1 (9-73)
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FUGITIVE DUST FROM
WESTERN SURFACE COAL MINES
by
Frank Cook, Arlo Hendrikson,
L. Daniel Maxim and Paul R. Saunders
Mathtech Division
Mathematica, Inc.
Princeton, New Jersey 08540
Contract No. 68-03-2477
Project Officer
Edward R. Bates
Energy Pollution Control Division
Industrial Environmental Research Laboratory
Cincinnati, Ohio 45268
U. S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
INDUSTRIAL ENVIRONMENTAL RESEARCH LABORATORY
CINCINNATI, OHIO 45268
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DISCLAIMER
This report has been reviewed by the Industrial Environmental
Research Laboratory-Cincinnati, U.S. Environmental Protection Agency,,
and approved for publication. Approval does not signify that the
contents necessarily reflect the views and policies of the U.S.
Environmental Protection Agency, nor does mention of trade names or
commerical products constitute endorsement or recommendation for use,.
r
4
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5
FOREWORD
When energy and material resources are extracted, processed,
converted, and used, the pollutional impact on our environment and even
on our health often requires that new and increasingly more efficient
pollution control methods be used. The Industrial Environmental
Research Laboratory-Cincinnati (IERL-CI) assists in developing and
demonstrating new and improved methodologies that will meet these needs
both efficiently and economically.
Total suspended particulate (TSP) levels were measured upwind and
downwind of mining and reclamation activities at four western U.S.
surface coal mines during three different climatic seasons.
Statistical analyses of the resulting data were conducted to identify
and explain variability in observed TSP levels.
For further information contact the Energy Pollution Control
Division. . • -• .
I;
David G. Stephan
P Director
[ J Industrial Environmental Research Laboratory
L Cincinnati
r
111
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ABSTRACT
Field measurement of fugitive dust levels were made 250 to 500
meters downwind of mining activities and areas at four surface coal
mines in the Northern Great Plains during three different climatic
conditions. Ambient dust levels were also monitored. Wide ranges of
temperature, wind speed, wind direction, precipitation, soil moisture,
and mining activity levels are represented in the field data.
Statistical data analyses have been extensively employed to
examine trends, test hypotheses and explore relationships. Key
findings are as follows. Mine-to-mine differences in average total
suspended particulates (TSP) levels were significant; the evidence for
seasonal differences is weaker but consistant with physical theory and
prior judgements. There are indications that snow cover, in
particular, is associated with lower TSP values. On the average,
downwind TSP levels were 35 percent higher than ambient (upwind)
levels, although ambient levels exceeded downwind levels during 29
percent of the sampling periods. Most mining activity levels, except
pickup truck operation, were positively correlated with TSP levels, but
only the following correlations were significant: dragline activity,
coal haulage, on- and off-mine traffic. Overall, wind speed raised to
the 0.4 power was the best predictor of TSP levels.
iv
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CONTENTS
f| Disclaimer • ii
s i Foreword ............. iii
n!
: \ Abstract iv
I ,; Figures . vii
: Tables ix
I / 1. Introduction 1
2. Conclusions . 2
I •
• 3. Recommendations 4
- i
I; i 4. Field Conditions and Procedures . 5
1 Introduction 5
The mines . . 5
The visits 5
'" Methodology of observations ..... 7
Sampling plan 7
Dust concentrations 11
f * Soil moisture 13
Weather conditions 20
Mining activities 23
, - Special dust control procedures 26
Missing data 27
5. Data Analysis 28
' Introduction .......... .... 28
Data and assumptions ...... 30
ANOVA: beginning of a search for structure ... 33
{"". ^ Numerical estimates of main effects and
f * ' interaction terms ........ 39
--": , The choice of a transformation 48
f-r. Review of relevant theory 50
!• Application to particulates data 52
iV . A return to the question of outliers 57
Differences among sampler locations 59
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CONTENTS (continued)
Relationship of TSP to activity levels 69
Details of regression results • 75
The distribution of particle size • • • 81
Bibliography . ..... 83
Appendices
A. Mine Maps • • 89
B. Coordinate Geometry . . 103
Point source 103
Line source 106
C. Literature Survey H5
Introduction ••• 115
Emission models H7
Particle size and deposition rates 130
Dispersion models •• 136
D. Field Data 142
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FIGURES
Number Pa?e
1 Plot of GAU"1 (TSP) versus log of TSP value
2
3
A-l
A-2
A-3
A-4
A-5
A-6
A-7
A-8
A-9
A-10
A-ll
A-12
A-13
B-l
B-2
Shape of likelihood surface for selecting
Scattergram (log-log scales) of TSP for ambient
samplers: data sets 1 and 2 with outliers
Map for mine 1, visit 1
Map for mine 1, visit 2
Map for mine 1, visit 3
Map for mine 2, visit 1
Map for mine 2, visit 3 .....
Map for mine 3, visit 1 ...
Map for mine 4, visit 1 • •
Coordinate geometry for a point source
Sampler distance from plume centerline
. , 54
. , 90
, . 91
, , 92
. . 93
94
. . 95
. . 96
, , 97
98
. . 99
, , 100
, . 101
. , 102
, . 104
. , 107
vii
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FIGURES (continued)
f'
' * Number Page
H: B-3 Distance from line source to sampler 108
B-4 Skewed area source geometry Ill
[ i.; C-l Fine particles are reflected 139
C-2 Tilted plume hypothesis 141
B
G
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TABLES
Number
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Selected Features of Mines Visited
(After Axetell) ....... •
Frequency Distribution of TSP Values ,
Moisture Data and Associated Computations
for Mine 1, Visit 1
ANOVA Computations for Moisture Data,
Mine 1, Visit 1
Minimum, Maximum, and Average Values
for Wind Speed by Mine and Visit
Minimum, Maximum, and Average Values
Mean and Extreme Stability Class
Raw Data and Estimates of Missing Data
Analysis of Variance Tableau for Exploratory
Analysis ANOVA 1: Mines and Locations Random,
Analysis of Variance Tableau for Exploratory
Analysis ANOVA 2: Mines, Seasons, and
Fag
, , 6
, . 8
, . 14
, . 17
, . 18
. . 19
, . 21
. . 21
. . 22
, . 22
. . 24
. . 25
. . 31
. . 35
. . 36
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TABLES (continued)
Number Page
16 Ancillary Tables - Sums of Raw Data .......... 37
17 Analysis of Variance Computations ... ........ 38
18 Analysis of Variance Tableau: Mines, Locations
Random and Seasons Fixed .............. 40
19 Analysis of Variance Tableau: Mines, Seasons,
and Locations Random ... ......... .... 41
20 Components of Variance Estimates: Mines and
Locations Random, Seasons Fixed ........... 42
21 Components of Variance Estimates: Mines,
Seasons and Locations Random ............ 43
22 Numerical Estimates of Main Effects and
Interaction Terms ......... ......... 44
23 Mean Values for "What If" Analysis .......... 47
24 Consequences of Deleting Data From Mine 1
Season 3 Un transformed Data - All Units
in yug/m3 ...................... 49
25 Numerical Search to Select Optimal Transformation . . 53
26 Analysis of Variance Computations: Three
Candidate Outliers Deleted, Transformed Data
(0 = 0.0) ............ ......... 55
27 Analysis of Variance Tableau: Mines, Locations
Random and Seasons Fixed: Three Candidate
Outliers Deleted, Transformed Data (0 - 0.0) .... 56
28 Analysis of Variance Computations: Three
Candidate Outliers Deleted, Transformed Data
(0= 0.0) ..................... 58
x
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TABLES (continued)
Number Page
29 Analysis of Variance Tableau: Mines, Locations,
.Random and Seasons Fixed: Three Candidate
Outliers Deleted, Transformed Data (0= 0.0) .... 60
30 Upwind and Downwind Sampler Analysis Data Set 1 ... 62
31 Paired t Test on Upwind Versus Downwind Sampler
Locations, Data Set 1, (Transformed Observations) . 62
32 Upwind and Downwind Sampler Analysis Data Set 2 ... 64
33 Paired t Test on Upwind Versus Downwind Sampler
Locations, Data Set 2, (Transformed Observations) . 64
34 Summary of Results for Upwind Versus Downwind
Analysis: Transformed Data 66
35 Table of Activity Numbers, Definitions for
Regression Analysis 70
36 Means and Standard Deviations for Regression
Activity Analysis (All Variables Transformed
as Natural Logarithms) 73
37 Simple Correlation Coefficients for Activity
Analysis 74
38 On the Trail of Multicollinearity: Simple
Correlation Coefficients Between Dummy Variables
for Mines and Trips and Activity Variables 77
39 A Summary of Selected Regression Results 78
40 Regression Statistics for Model Selection 80
41 Regression Output for Combined Activity and Site-
and Season-Specific Analysis ,82
C-l Soil Properties (Agricultural Tilling) 126
C-2 Estimated Vs. Actual Emissions (Agricultural
Tilling) 127
xi
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SECTION 1
INTRODUCTION
[I Over the years 1969 to 1977 production of coal by the surface
\1 mining method in the western United States has grown at a compound
; annual growth rate of 27%. Annual production for a seven-state western
H area,, which was approximately 110 million tons in 1977, was only 16
il; million tons in 1969. (McNeal et al. 1978.) Continued growth is
< anticipated.
H| Most strippable western coal is located in semi-arid, high plains
; areas characterized by sparse vegetation, erodable soils, and high
J winds. High ambient dust levels are a combined result of these
| ;; factors. Disturbance of land by surface coal mining may worsen these
I---'> dust conditions.
I :; There exist theoretical models for use in estimating the
(^ i dispersion patterns for particulate matter emanating from a point
; source, such as a powerplant stack. More recently, attempts have been
p: made to model emission, dispersion, and deposition of fugitive dust
h( from point and non-point sources typical of those from western surface
coal mines. To date, however, there have been few attempts to apply
,,: statistical techniques to determine empirical relationships between
suspended particulate levels in mining areas and explanatory variables,
'-! . such as mining activity levels and meteorological variables. This
study employs 'such techniques to examine the effects on air quality of
[*' mine operations under various meteorological and operational
[;- conditions.
3
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. CONCLUSIONS
The following conclusions were derived from statistical analysis
of suspended particulate data gathered using four high-volume air
samplers at four western surface coal mines on three trips during the
periods late May to late June, 1977, early November, 1977 to early
December, 1977, and late December, 1977 to early February, 1978.
Roughly 80 percent of the total suspended particulate values were
obtained 250 to 500 meters downwind of mining areas and activities, the
other 20 percent being upwind or "ambient". Prevailing winds during
sampling periods were westerly," averaging 5"to 14 miles per hour, and
gusting to 35 miles per hour. Ambient temperature values averaged
between -20 and 62 degrees Farenheit. Sampling took place during dry
periods as well as during periods of rain and snow.
Findings and conclusions are summarized below.
1. The average TSP level for all samplers over all mines
and all trips was 157 micrograms per cubic meter
(//g/m^). (Summaries of the meteorological conditions
under which these were obtained can be found on pg.
24 ejt seq..) Percentile values were the following:
- Percent of
Observations
Below TSP Value TSP Value (MS/m?)
"' " "~
2.5 22
50.0 110
97.5 620
The average lies above the median value of 110
because readings below the average were more
t frequent; that is, the distribution of TSP values is
' skewed to the right.
2. Mine-to-mine differences in average TSP levels were
highly significant. Mine averages ranged between 115
and 234 Mg/m3. But site-specific differences other
than the types and levels of mining activities were
;_ more important in explaining TSP variability than
were the activity levels themselves.
2
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3. There is evidence to suggest that there are seasonal
differences in average TSP levels. Such evidence falls
short of unequivocal proof, however.
4. Differences in average TSP levels among samplers were
significant. On average, downwind TSP levels exceeded
ambient levels by 35 percent. (The 95 percent confidence
interval on this difference is 8 to 64 percent.) On 29
percent of the observations, however, upwind or ambient TSP
levels exceeded those of all three downwind samplers. This
reflects the complexity of transport phenomena. The level
of dust not due to mining activities evidently at times is
higher upwind than downwind.
5. All mining activity levels correlated with TSP levels in
the positive direction, except precipitation (expected) and
pickup truck traffic at the mines (unexpected). Possible
causes of the unexpected negative correlation are discussed
in Section 5. Statistically significant correlation
occurred between TSP levels and the following variables:
• Wind speed
• Frequency of dragline operation
• Frequency of coal haulage from
; the pit
• • Frequency of pickup truck traffic
at the mine
• Amount of traffic on unpaved public
road adjacent to mine sites.
Of these, wind speed is the strongest predictor of
TSP levels.
6. The relationship between TSP level and explanatory
variables, derived from statistical analysis of data
pooled over all mines, field trips, and samplers, is:
TSP = 30.3 Q?'13 Q°'10 Q°-10 S0'40
where TSP = average TSP (jug/m3)
i ' .
, Q- = dragline operating time
• per shift (hours)
Q 2 - number of coal haulage
truck trips per shift
Qg= number of vehicles driven
past the mine on unpaved
public roads, per shift
S - wind speed (mph)
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SECTION 3
RECOMMENDATIONS
It is recommended that a large-scale, long-term experimental
program be conducted to develop and validate empirical relationships
between total suspended particulate levels in western surface coal
mining areas and explanatory variables which measure the charac-
teristics of the real dust sources found at such mines. Total
suspended particulate levels should be measured at two or more mines
over a period long enough to ensure that wide range of mining and
meteorological variables are observed. Empirical results should be
systematically compared to those estimated using published emission
factors and dispersion/deposition assumptions.
11
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C
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CT,
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SECTION 4
FIELD CONDITIONS AND PROCEDURES
INTRODUCTION
This chapter details the measurement devices, field procedures and
relevant limitations of the data collection effort.
This chapter also contains frequency distributions and other
relevant statistics calculated from the raw data which is useful for
data interpretation.
In brief, the data collection plan included three visits at
different seasons to each of four surface mines. Four high volume air
samplers were used to measure dust concentrations during these visits.
Additional data was collected on soil moisture, weather conditions and
activity levels of various mining or mining related operations. These
data are candidate explanatory factors to describe the observed
variability in dust concentrations.
THE MINES
Observations were taken at several sites within four mines. The
mines were selected from among nine that had been surveyed on a
previous EPA contract (No. 68-03-2226). Willingness of mine operators
to permit dust measurements to be taken was an important selection
criterion.
Table 1 identifies the region in which the mines were located and
presents a brief description of the topography, vegetative cover and
soil type at each mine. Axetell (1978 p.6) found that estimates of
fugitive dust emissions for a particular mining operation can vary
widely among mines. To facilitate comparison, the code-letter of the
most similar mine in Axetell's study appears in Table 1.
Mine maps drawn from aerial photographs are contained in Appen-
dix A,. These maps show the location of mining activities, samplers,
soil moisture observations, the weather station and unvegetated areas
for each visit.
THE VISITS
To help ensure that a wide variety of operating conditions was
observed and, additionally, to study the effect of seasons of year on
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TABLE 1. SELECTED FEATURES OF MINES VISITED (AFTER AXETELL)
Mine
1
2
3
4
location
Eastern
Montana
Central
North Dakota
Central
North Dakota
Eastern
Wyoming
Terrain
Boiling
Semi-rugged
Semi-rugged
Rolling
Vegetative
cover
Grassland
Sagebrush
Sagebrush
Grassland
Soil type
(surface)
Clayey
Loamy
Loamy
Sandy,
clayey
Mine
cover
E
D
D
C
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narticulates values, three visits were made to each mine. The field
tSm visited each mining operation sequentially. The first round of
visits took place in the spring of 1977. The second took place that
fall, and the third and final visit was made during the winter of
1977-78. Table 2 provides the dates for each of the visits. Visit
durations ranged from 3 days to one week.
METHODOLOGY OF OBSERVATION
Variables measured during the mine visits included dust
concentrations, meteorological variables such as wind speed and
direction, temperature and precipitation, soil moisture and finally
quantitative data on the pattern and intensity of mining activities.
Additional data gathering included the acquisition of aerial
photographs from which maps could be drawn. The collection plan is
described below.
SAMPLING PLAN
;~~4-~v.~ m,c,4- Vu= r»(~ineiric»-rpad in the desien of a sampling
Some
Several factors must be considered in the design of a sampling^
plan for measuring the atmospheric concentration of particulates.
of these are:
(1) Emissions sources to be measured.
(2) Direction of the air sampler from the source.
(3) Distance of the air sampler from the source.
(4) Duration of sampling interval.
Some options for sampler use during a given day are: (a) one 24-hour
sample, (b) two 12-hours samples, and (c) three 8-hour samples. It was
determined that air samplers be operated for eight-hour periods between
filter changes. Three reasons for the choice of this period are:
(i) More data is available for estimating the parameters
in a model when shorter sampling periods are used.
(ii) Wind speed and wind direction are so variable during
a 24-hour period that one of the 8-hour periods can
be expected to account for the majority of the dust
collected from the mine. By using shorter periods,
more accurate estimates can be made of the dust
arising from different wind directions and wind
speeds.
(iii) Dust emissions are dependent on mining activities,
which vary with 8-hour shifts. Thus, the sampling
periods should match as closely as possible changes
in shifts. This is particularly true for samplers
downwind from sources where changes in activity take
place at the end of a shift.
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TABLE 2. MINE VISITS AND DATES
Mine
Visit
Date
Fran
1
2
3
1
2
3
1
2
3
1
2
3
24 May 1977
21 November 1977
19 December 1977
8 June 1977
8 November 1977
11 January 1978
8 June 1977
15 November 1977
17 January 1978
21 June 1977
30 November 1977
30 January 1978
27 May 1977
23 November 1977
21 December 1977
11 June 1977
11 November 1977
14 January 1978
15 June 1977
18 November 1977
29 January 1978
24 June 1977
3 December 1977
2 February 1978
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The goal is to place the monitors in such a manner that a profile
of the concentration of particulates can be obtained. Sampler
locations and sampling intervals are restricted to areas and intervals
such that sufficient amounts of particulates will be collected to give
reliable estimates of the air concentration at the receptor point. We
first consider sampling intervals.
The hi-vol samplers have a sampling rate of 20 c.f.m. or 34
meter3/hr. If the air concentration for particulates of a size
collected by one of the four filters is c wg/m3 then during a period
of eight hours, a total of (2.72 X 10-4) c grams will be collected in
the filter. Thus, if the concentration is 37 jug/m3, 0.01 grams will
be collected in an eight-hour period.
The value 37 Mg/m3 is representative of the annual average total
suspended particulates in a nonurban atmosphere. The maximum
concentration may exceed this value by a factor of 10. To obtain
reasonable amounts of dust, the air samplers should be located close
to major sources of dust from the mine. Mathtech determined that
samplers should be placed within a maximum distance of 1000 meters
from a source in order to measure emissions. Contributions to air
quality at remote distances could be estimated through dispersion/
deposition modeling.
The placement of samplers is dependent on the predominant wind
direction. Although the air samplers are portable, it was not
practical to move the air samplers more than once every 24 hours.
Because wind direction is variable within 24 hour periods, sampler
locations were approximated prior to the sampling period by
determining the predominant wind direction for that season.
The following is an outline of the air sampling program as
conducted by Hittman Associates' field team.
Pretrip Requirements
A. Check out and test high volume air samplers,
generators, and weather station to insure
workability. Order required replacement and spare
parts. Each high volume air sampler should have at
least one extra set of brushes for the motor since
this part is known to wear out rather rapidly.
B. For ease of handling after sampling, each filter will
be placed in a suitable plastic bag, and the bag will
be sealed. To obtain pre-sampling weights of bags
and filters:
1) Number each filter and corresponding plastic bag
in the lower right-hand corner.
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" 2) Weigh each filter and corresponding plastic bag
together to the nearest 0.0001 gram.
3) Place filter in corresponding bag and seal.
Sampling Procedure
..._ A.—Sample each mine in consecutive order.
B. Set up four high volume air samplers and generators
at each mine at locations designated by Mathtech.
C. Change filters at designated eight-hour intervals.
D. When each filter is removed, place it in its
like-numbered plastic bag and seal.
E. Record filter number, location, time interval, data,
and other pertinent information about the filter on
. forms to Jbe provided. - -- — — — -- ,,-.,...
F. Record activity at mine at specified intervals on
mine activity data sheets to be provided by Mathtech.
G. -Change locations of high volume samplers on a daily
basis to new locations as requested by Mathtech.
Post Sampling Analysis
A. Open plastic bags and let sit at ambient conditions
for 24 hours.
B. Weigh bag and filter to the nearest 0.0001 gram.
C. Report difference in weight on forms provided.
A somewhat modified version of this plan was put into practice.
One imporant modification was to the sampler placement design.
Ownership of the surrounding land, topography, and accessibility of
samplers to refueling trucks limited the area in which samplers were
placed. Selection of sites could not be made solely on the basis of
preclominent wind direction and activity location. In addition, the
variability of the wind direction argued for a more ad hoc placement
in order to obtain an ambient (upwind) reading.
Originally, one visit per season was planned. Delays in securing
permission to enter mine premises to conduct the measurements delayed
the spring visit until June. It was decided that this visit should
serve as a combined spring-summer visit.
The modified plan as executed is described in detail below.
10
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DUST CONCENTRATIONS
Dust concentrations were measured with General Metal Works GMWL
2000 high volume air samplers. These draw in particulate matter with
diameters under 100 microns and pass them through a graded series of
paper filters. In this experiment, the filters trapped particles in
the size ranges of 0-1.1 microns, 1.1-2.0 microns, 2.0-3.3 microns,
3.3-7.0 microns and 7.0 microns or larger. The samplers were driven
by small gasoline generators which were positioned so that their
exhaust would not enter the sampler's intake.
Further details of the sampling procedures are given below:
(1) The filters used for this study were glass fiber discs
(Anderson head) and rectangles (backup) chosen for their light tare
weight and non-hygroscopic properties.
(2) Preparation of filters for field sampling - Anderson head
and backup filters and bags were numbered with a marking pen and were
allowed to dry overnight in plastic garbage sacks to prevent
contamination prior to weighing. Filters and matching bags were
weighed together on a Mettler balance, rated accurate to 0.0001 grams.
A zero adjust check was made every 25-30 samples to insure accurate
weights. Each filter and bag was handled, at all times, with plastic
surgical gloves to prevent dirt and oil contamination, and was
returned immediately after weighing, in a sandwich arrangement, to its
respective plastic sack. The sacks were'placed in cardboard boxes to
prevent rubbing between filters and bags, and the boxes, in turn, were
place! in a footlocker to cut down on dust settling on the outside of
the sacks. The sacks remained sealed except during brief periods
while changing filters.
(3) Description of sampler operation and procedure - Generators
were placed downwind from the sampler at a distance of 25 ft. (one
extension cord length). Each sampler consisted of a General Metal
Works hi-volume air sampler combined with an Anderson 2000 particle
sizing sampler, calibrated for 20 cfm. use. The operations manual
provides additional features of this device. The areas around the
sampler intake and the heads themselves were cleaned with acetone
prior to each field trip. Heads were kept in plastic bags to insure
cleanliness while in transit.
All hi-vols were equipped with pressure recorder clocks. In the
event a generator would cease operation, these clocks recorded the
time period during which the samplers operated.
%
To supply the generator with sufficient gas to run for an 8 hour
period, a hose to a small metal barrel was attached to the generator
tank and gas was dripped into the generator tank at a slow rate.
The Anderson head was loaded with filters in the back of a pickup
truck protected by a fiberglass camper shell. All operations
11
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involving contact with filter media were conducted while wearing
plastic gloves. The Anderson head and backup filter were then
transported to the sampler and fastened in place.
Calibration of the Anderson head/hi-vol unit to 20 cfm. was
conducted as follows. First the high vol was turned on until air flow
stabilized (usually a few seconds). Then an oil filled manometer
(described in the operating manual), read in inches of water, was
attached to a pin hole orifice protruding from the lowest stage of the
Anderson head. A laboratory calibration curve provided for each head
was used to compensate for elevation differences. The elevations for
each sampler were obtained from contour maps in the mine office. The
manometer reading, thus obtained, was used when regulating air flow
thru the samplers.
With the manometer attached to the Anderson head, the air flow of
the sampler was controlled by one or two voltage regulating "pots".
These pots were manipulated until the reading on the manometer matched
the correct reading determined by the curves. The manometer is then
removed - the operation completed. (Note: A constant flow of 20 cfm.
is maintained by a probe located between the filter assembly and the
motor/blower unit.)
(4) Storage of samples - Each filter had its corresponding
plastic bag. The collected sample and filter were placed in this bag
and sealed to prevent contamination and escape of sample. These bags
were stored in a large plastic sack for the duration of the field trip
and remained in the sack until they were weighed.
(5) Obtaining final filter weights - The plastic sacks
containing samples frcm each trip were transported to the analytical
balance for weighing. The same scales were used throughout the study
when measuring before and after weights. Again, each bag was handled
and weighed while wearing plastic gloves. The zero adjust on the
scales was monitored every 25-30 samples as before.
Four samplers were available for deployment on each visit. They
were placed at locations determined by the prevailing wind directions
at each mine during each season of observation. Naturally, these
locations changed frcm visit to visit. The measurement plan called
for one sampler to be placed upwind of all mining activities to
measure ambient dust concentrations, though variability of wind
direction sometimes placed the "ambient" sampler downwind of some of
the mining operations. Contaminated "ambient" sampler readings were
selectively excluded from analysis, as described in Section 5. The
other samplers were placed downwind of major dust sources such as
draglines, blasting, and spoil piles at distances ranging from 250 to
500 meters.
*
The observation plan called for one set of concentration measures
for each eight hour working shift at the mine. The samplers were
allowed to run for periods varying from one and one-half to six hours.
12
-------
At the end of each observation period the filters were removed and
weighed and the accumulated dust converted to a concentration in units
of micrograms per cubic meter.
The lengths of sampling periods were chosen to ensure that the
observed concentrations would be representative of typical mine
conditions. One consequence of the longer sampling periods was that
the wind would sometimes shift direction during the sampling period.
Some changes in direction caused different samplers to be upwind of
mining activities at different times within the sampling period. When
this occurred, no measurement of the ambient level of concentration
was possible.
The number of observations for which there was clearly an ambient
sampler was reduced by a number of other factors. Wind direction, for
example, was measured hourly. During some hours the direction varied
enough that no meaningful single figure could be assigned. At times,
the wind was calm. The Gaussian plume dispersion model predicts that
when the wind is calm dust emissions remain at their sources and the
concentrations build to infinity. 'In reality the Rrownian motion of
the air molecules and thermal convection currents will cause the dust
to disperse, but this has not been modeled and one cannot determine
which if any of the samplers receive dust from on-mine sources. For
some wind directions, none of the four samplers was upwind.
Particulate values in each size range were aggregated to give
total suspended particulates (TSP). TSP values over all samples
during all shifts at all visits to all mines averaged 157 micrograms
per cubic meter and ranged from 10 (the smallest observed) to 1,600
(the largest observed). Approximately 95% of the observations fell
beneath 450,ug/m3. Table 3 shows the frequency distribution of TSP
values including cumulative percentage figures and the standardized
normal variate (GATJ-1) corresponding to the empirical cumulative
distribution function. (As can be seen, the overall distribution is
asymmetrical, being skewed to the right.) The standardized normal
variate gives the values which would occur in the center of each
interval if TSP had a Gaussian (normal) distribution with mean zero
and the observed standard deviation. A graph of these values versus
the actual values would yield a straight line if TSP had a normal
distribution. Figure 1, a plot of log TSP vs. GAIT1 (TSP), indicates
that the distribution of TSP values is instead roughly log-normal.
Analyses reported in Section 5 of this report show that the residual
variance in TSP after removing systematic effects are indeed
log-normal, and that this transformation stabilizes the residual
variance.
SOIL MOISTURE
Soil moisture as percent of total weight was recorded at
locations designed to reflect diverse soil conditions: haul roads,
the pit and bench, off-mine roads, topsoil or spoil piles, areas of
contouring or reclamation, and the surrounding landscape. (It should
13
-------
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14
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Figure 1. Plot of GAU'1 (TSP) versus log of TSP
value confirms long tail distribution.
15
-------
An analysis of variance
term. Specifically, suppose that:
yij='i^i + "j + eio
where y • • = value of soil moisture from location i
wnere y -^ collected during shift j
._ ____ M. _= .overall .mean of -the observations - - - ....... ,T .....
^ . = effect due to ith location
n = effect due to jth shift (time)
'3 ,.,;..;•-
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ss asr i
smaller range of effects of f rom tlo**T° th the spatial effects
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or
of the shift in which measured.
^^"k^ir^otL^scir-SirtSI SSdl««. . dust.xevels
on dust.levels
can be found in Section 5.
16
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19
-------
WEATHER 'OONDiTiONiS'
A mechanical weather station built by Meterology Research, Inc.
monitored wind speed, wind direction, temperature and precipitation at
each site. The manufacturer certifies the readings to be accurate
between .75 and 125 mph., and -60° to 120°F. The weather station drove
•a-chart recorder from which readings were taken on an hourly basis.
The-portable weather station -sites were chosen mainly on the basis of
topography. High, extensive flat land was preferable because winds
blowing from any direction would not be affected by barriers, and would
more truly represent the conditions at the mine site. The station was
oriented to true north by aligning precut notches on the base of the
station with a compass reading (magnetic declination corrected).
Summary weather data on wind speed, wind direction, temperature,
precipitation and atmospheric stability is presented in Tables 7-11.
Table 7 lists the minimum, maximum and average wind speed observed
at each mine during each visit. The speeds range from calm to a
maximum of 34.5 miles per hour, and average about nine miles per hour.
No clear-rcut relations between wind speed and mines or visits emerge.
Three of the four mines show different orderings of wind speed over
visit - the widest spread possible among three visits. Similarly, no
one mine had higher wind speeds throughout. It is apparent that
multicollinearity did not occur between wind speed and mine or visit.
Wind direction varied considerably at each mine within each visit,
as Table 8 demonstrates. When the minimum and maximum angles are
close, the swing frequently occurred the "long way around", that is a
more than 180° shift. This variability need not reflect gradual
changes. Wind direction shifted by as much as 165 ° during the course
of a four-hour sampling period. During some hours the variability was
sufficiently great that no direction could be assigned. No direction
was assigned to hours when the wind was calm.
Temperatures, of course, have a strong seasonal dependence. The
highest temperatures occurred during the spring visits. Temperatures
in the fall and winter visits were closer than between the fall and
spring visits. Table 9 presents minimum, maximum and mean temperature
values for the various visits.
Snow cover and precipitation data is shown in Table 10. All of
the mines lay under a substantial snow cover during the third visit.
During the second visit, mines one and four had snow, although the
cover at mine four was melting the last day of the visit.
Mine'2 clearly experienced the most frequent precipitation. This
appears to have been a random occurrence. Mine 3, physically proximate
and studied only a few days later on each round of visits, shows much
less precipitation. Mine 4 appears the driest on each visit. At each
mine, the winter trip saw the greatest frequency of precipitation.
20
-------
TABLE 7. MINIMDH, "MAXIMUM,, AND AVERAGE VALUES FOR WIND SPEED BY MINE AND VISIT
Mine
Visit
Min. wind., speed Max. wind speed Mean wind speed
(kph) (mph) (kph) (mph) (kph) . (mph)
1 1
2
3
2 1
2
3
3 1
2
3
4 1
2
3
2.7
.97
3.9
3.4
2.9
0.0
4.9
0.0
1.6
1.9
2.3
1.9
1.7
.6
;2.4
4.1
1.8
0.0
3,0
0.0
1.0
1.2
1.4
1.2
33.8
24.6
43.4
38.3
33.6
29.0
43.1
55.5
27.4
32.7
32. 2
32.1
21.0
15.3
27.0
23.8
20.9
18.0
26.8
34.5
17.0
20.3
- 20.0
24.8
12.9
10.9
23.2
20.6
14.6 .
8.5
15.8
18.5
13.8
15.0
17.2
15.3
8.0
6.8
14.4
12.8
9.1
5.3
9.8
11.5
8.6
9.3
10.7
9.5
TABLE 8. MINIMUM, MAXIMUM, AND AVERAGE VALUES FOR WIND DIRECTION
Mine Visit
1 1
2
3
2 1
2
3
3 1
2
3
4 1
2
3
Minimum
angle
30.
0.
180.
0.
0.
0.
10.
0.
75.
0.
70.
0.
Maximum
angle
360.
355.
290.
355.
340.
360.
350.
359.
295.
359.
345.
320.
Mean
angle
260.6
289.5
255.3
278.3
278.6
312.8
265.4
305.4
196.6
338.3
249.7
270.7
Angles are given in degrees from due north.
(i
21
-------
i:
TABLE 9. MINIMUM, MAXIMUM, AND MEAN FOR TEMPERATURE
0:
Mine Visit
1 1
2
3
2 1
2
3
3 1
2
3
4 1
2
3
Min.
temperature C
8.8
-29.4
-21.0
8.8
-12.2
-28.8
7.2
-9.4
-31.6
7.7
-13.8
-11.1
Max. _
temperature C
32.2
-17.. 7
-6.1
26.7
1.1
-6.1
23.3
11.1
-22..2
27.7
5.5'
-3.3
Mean
temperature C
16.9
-23.3
-14.8
16.7
-6.7
-17.0
12.3
1.8
-28.6
16.1
-5.9
-13.6
IT
ei
TABLE-10. PRECIPITATION..AND SNOW COVER DATA
Mine Visit
1 1
2
3
2 1
2
3
3 1
2
3
4 1
2
3
Hours of
precipitation
3
23
24
18
0
24
3
2
5
0
3
3
(rain)
(snow)
(snow)
(rain)
(snow)
(rain)
(rain)
(rain)
(rain)
(snow)
Hours of
observation
70
90
73 '
73
73
74
36
62
75
48
72 '
72
Snow cover
(cm)
0
10.2-15.2
10.2-35.6
0
0
10o2-30.5
0
0
15.2-35.6
0
2.5-10.2
0
o
22
-------
The final weather observation concerned atmospheric stability
class. Table 11 shows the mean and extreme Pasquill-Gifford stability
classes observed at each mine on each visit. The Pasquill-Gifford
scale classifies the depth of atmospheric turbulence into six
categories. In general, the more stable the atmosphere, the greater
the vertical dispersion of a dust plume (Busse and Zimmerman, 1973).
The table represents class A, the least stable, as one and class F as
six. While the means are similar, the ranges vary considerably. Mine
2 during visit one has a mean stability class of D, the result of a
dirunal variation between C and E. Mine 3 during the second visit
showed the same mean, but oscillated between B and F. No prominent
differences emerge between mines or visits.
MINING ACTIVITIES
Mining activities were recorded during shifts when dust sampling
was active. Twelve potential dust producing activities were observed:
dragline operation, coal haulage, vehicular traffic on mine roads,
vehicular traffic on nearby public roads (usually unpaved), water
trucks, scraping, grading, coal loading, coal unloading, blasting and
drilling of coal, and drilling of overburden.
The numbers and durations of mining activities varied
considerably among mines and across visits. The minimum number of
activities observed during the course of a visit was zero; mine 3 was
inactive on the first trip. (The inclusion of this data does not
lower the estimate of the effect of mining activities. Rather, it
helps to predict and correct for the background dust levels due to
entrainment of soil by wind in the absence of activities.) The maximum
number of activities ever simultaneously conducted was nine. Twelve
activities in all were recorded.
Each activity was assigned a measure of duration or intensity.
For dragline operation, coal or overburden drilling, scraping or
grading, the measure was the number of operating minutes per hour.
When operating times were recorded per shift, it was assumed that the
operations were uniformly distributed over each hour. Inaccuracies
created by this assumption were later reduced when the hourly figures
were aggregated over each sampling period, since the periods
approximated the.shifts in length. Coal haulage and watering were
recorded in terms of truck trips. Vehicles on haul roads or other
on-mine locations were measured in terms of vehicle-miles driven.
Since mileage figures could not be obtained for traffic on public
roads, the number of vehicles was recorded. Loading and unloading of
coal was logged in terms of tonnage. Blasting was measured by the
number of.holes shot.
Table 12 shows the total duration or intensity of each mining
activity aggregated over all shifts observed on each visit.
All mines but one operated three shifts a day, seven days a week.
Mine 1 operated only one shift per day. The dragline was the piece of
23
-------
C:
i
I:
TABLE 11. MEAN AND EXTREME STABILITY CLASS
Mine
1
2
3
4
Visit
1
2
3
1
2
3
1
2
3
1
2
3
Least stable
class
2.0
1.0
3.0
3.0
1.0 ' .
• 1.0
3.0'
2.0
2.0
1.0
2.0
2.0
Most stable
class
6.0
6.0
6.0
4.0
6.0
6,0
6.0
6.0
6.0
5.0
6.0
6.0
Mean class
3.7
3.8
4.2
4.0
3.9
3.7
4.2
4.0
4.3
3.1
4.0
4.1
24
-------
-------
equipment most often in operation, followed by loader and tipple.
Blasting was done only during the daytime at all mines.
SPECIAL DUST CONTROL PROCEDURES
Each of the mines employed some dust control procedures. These
measures, obtained from observation and interviews with mine operators
are briefly described below:
(1) Mine 1: A large watering truck of some 10,000 gallon
capacity was observed in operation during preliminary
site surveys. The road was watered from the pit
where the trucks were loaded to the truck dump point
near the mine office. Watering is reportedly done on
an as-needed basis. On windy days, watering may be
continuous. A cover crop is used on the topsoil
stockpiles: oats in the spring, sudan grass in the
summer and rye in the fall. This mine was inactive
during the first visit and there was snow cover
during the second and third. Therefore, watering did
not occur at this mine during the actual dust
measurements.
(2) Mines 2 and 3: A cover crop is used on topsoil
stockpiles. The cover crop, oats and sweet clover,
is planted in the spring. Roads are watered during
the times of the year when dust is thought to be a
problem (chiefly July and August). There are two
water trucks. One is used to water the coal haul
roads. The other is used on the roads used for
topsoil haulage. The trucks work 2 shifts per day.
The mine operator is considering using the topsoil
watering truck three shifts per day. A cover crop is
used on topsoiled areas. The cover crop, oats, can
be planted in the spring only. Areas which are
topsoiled too late for oats must wait until fall for
planting. Areas planted in the fall have no cover
until the following spring. This summer (1978) they
are going to try sudan grass on the topsoiled areas.
(3) Mine 4: After the spoil is graded, it is ripped.
Similarly, after the topsoil is applied it is ripped.
This is repeatedly done for several reasons - to
reduce compaction, to increase water infiltration or
the reservoir capacity of the spoil, to make a better
* bond between the spoil and the soil, and to reduce
wind erosion. The roughness of the spoil and soil
causes an eddy effect which reduces the erosive force
of the wind. No special dust control procedures are
used to control erosion on the topsoil stockpiles.
There is quite a bit of volunteer vegetation,
however. Also, the vegetation is removed with the
26
-------
"topsoil."'This vegetation, particularly the roots,
helps hold the soil together.
MISSING DATA
Not all the data prescribed by the experimental design was
collected. This resulted from equipment failure and bad weather.
For example, occasionally a dust concentration observation in
some size range had to be discarded because the filter froze to the
sampler. In this case, the total concentration over all sizes was
defined to be missing. Soil moisture data could not be collected when
the ground was frozen; hence no moisture data exists for the third
round of visits. There was no activity to record during the first
trip to mine 3.
On a few occasions, mechanical failures in the high volume
samplers prevented data collection. Specifically, this reduced the
number of dust concentration observation locations from four to three
during parts of. two visits. Some weather data were lost when
lightning struck a weather station.
Only the frozen ground during the third visit and the lack of
activity during the first visit to mine 3 could affect data analysis
in any systematic way. The remainder only reduce the number of
observations.
0
27
Li
-------
SECTION 5
DATA ANALYSIS
INTRODUCTION
This chapter summarizes the results of various statistical analyses
of the data collected as part of this study. Specifically, the opening
sections of this chapter describe the results of several analyses of
variance conducted to determine what, if any, differences in
particulates values measured at various locations, mines and seasons,
can be held to be statistically significant. Estimates of main and
interaction effects together with a complete components of variance
analyses are furnished. Results of these analyses have been summarized
in Section 1 and are set forth in detail in the following sections, but
briefly,
(1) significant differences exist in total particulates
at the four mines examined, mine 1 had the largest
and mine 2 had the smallest level of particulates,
(2) differences among seasons are obscured to some
degree by sample variability. However, there are
clear indications that snow cover, in particular,
acts to suppress (or is associated with lower)
emissions,
(3) initial analysis shows significant interaction
effects to exist between mines and visits. If some
activity related adjustments are made, the
interaction effects are lowered substantially and a
significant seasonal effect emerges and finally,
(4) significant differences exist among various sampler
locations for a given mine visit. This is later
shown to reflect ambient vs. downwind differences.
The next sections of this chapter use the constructive method of
Box and Cox (Journal of the Royal Statistical Society, 1964, see
bibliography) to show that a logarithmic (log) transformation of the
data is appropriate in that the assumptions which underlie the
statistical techniques are more nearly met if the data are transformed
prior to analysis. Specifically, the use of the log transformation
makes the deviations from predicted values approximately normal
(normality) and with constant variance (homoscedastic). The power law
* ' 28
-------
transformation also acts to remove possible outliers in the data base.
Replicate analysis with and without candidate outliers shows the above
conclusions are not sensitive to this consideration.
The next sections explore and detail further the finding of
differences among samplers. It is shown that these differences arise
largely between the ambient sampler and those downwind of the mining
operation - though the difference is statistical rather than
one-to-one. That is, measured particulate values from downwind
samplers were sometimes lower than corresponding upwind (ambient)
samplers. This implies that the level of dust not due to mining
activities was at times greater upwind of the activities than
downwind. Despite this, the mean particulate values corresponding to
the downwind samplers is some 30+% higher than background values. The
95% confidence interval on this increment ranges from about 8% to 64%
higher than upwind.
An analysis of the correlates of ambient particulates is
presented next. Regression analysis shows that ambient particulates
are positively correlated with wind speed (to the 0.34 power) and
negatively correlated with snow cover, though the sample size is not
sufficiently large to achieve statistical significance.
The concluding sections of this chapter explore the relationship
between particulates and activity variables at the mining operations
visited. Though obscured by problems endemic to observed rather than
designed experiments, a significant relationship is found. Key
variables which emerge as statistically significant predictors of TSP
include wind speed, vehicle counts on nearby public roads, on-mine
vehicles (though with an incorrect sign, an_anamoly that is explained
JLn jthe discussion of _multic_ollinearity on pages 12 arid "76), ""coal haulage
and dragline operation. ""The"overall multiple" correlation'coefficient was
0_.58 - highly significant if not .spectacular.. .Ajparallel__
site-specific analysis shows" that mine ""to mine differences "are at"
least as important if not more so than the quantitative activity
levels measured. Consideration of site-specific effects (dummy
variables associated with each mine and each visit) raised the
multiple correlation coefficient to about 0.65.
In all, the analyses demonstrate significant trends, differences
and relationships which are generally in accord with physical theory,
intuition and the findings of other investigators. These effects are
submerged in considerable variability, however, reflecting the
substantial contribution of experimental variability and other
(unmeasured) exogenous factors.
The results are useful, nonetheless, as illustrative of the
relationships likely to be found in practical situations where many of
the variables are not controllable. The statistical analyses may also
be of use and interest to future investigators as a paradigm for
exploration. (In a more technical sense, of particular interest are
the methods used to treat missing observations, various schemes to
29
-------
evaluate additivity, normality and homoscedasticity and the exposition
of various adjustment schemes to eliminate or minimize interaction
effects.) The next paragraphs are ventured to facilitate understanding
of the presentation of the statistical analysis.
The analysis described in the following sections is presented in a
sequence that parallels the sequence of exploration. Thus, for
example, an overall analysis of variance is first conducted to detect
what might be termed 'gross structure1 in the data, i.e., are there
differences among mines, visits, etc. For this purpose other variables
(such as mine activity levels) are omitted and submerged into the
'noise1 of measurements. Later, in an exploration of 'fine structure',
these variables are brought into the analysis to help explain or
rationalize findings of the preliminary analysis. This method of
exposition should be of value to future investigators seeking to
develop a strategy for data analysis.
Finally, statistical computations and conventions which are
commonly employed, for example, the layout of an analysis of variance
table, are omitted in the interests of brevity and continuity. Useful
textbooks or articles are included as references should the reader wish
a more detailed presentation.
DATA AND ASSUMPTIONS
This section reports the results of preliminary statistical
analysis of the data, in particular, an analysis of variance (ANOVA) on
total particulates. These data consist of measurements of particulates
in various size ranges at four different sampler locations for several
operating shifts in each of three visits (late summer, fall, and early
winter) to each of four mines. Complete data is shown in the appendix.
The data are first aggregated over all size ranges to total
suspended particulates (TSP). Table 13 shows these computed quantities
for the first seven sampling intervals for which data in all size
ranges was available. As explained earlier, operational limitations
resulted in some missing data for certain shifts, locations and visits.
Following generally accepted practice (see for example the Bennett and
Franklin reference in the bibliography), missing values were estimated
by appropriate row means to simplify subsequent analysis. Thus, for
example, only five particulate data points were available in the fourth
location" in the first visit to the first mine. In Table 13, these
quantities are estimated as the mean (114) of the preceeding five
observations. These estimated values and all other missing values
which were estimated are circled in the data matrix. As a second
example, operational difficulties precluded data collection from
location four in the second visit to the first mine. For purposes of
the analysis of variance, the seven missing values are estimated as the
mean across all sampler locations and sampling intervals for this visit
to this mine. (Corresponding adjustments are made to degrees of
freedom in the ANOVA to account for the use of estimated quantities for
missing observations.)
30
-------
TABLE 13. RAW DATA AND ESTIMATES OF MISSING DATA FOR FIRST SEVEN SAMPLING INTERVALS
Li
Sampler
Mine Visit Location
1 11
2
3
4
2 1
2
3
4
3 1
2
3
4
2 11
2
3
4
2 1
2
4
5
3 1
2
3
4
Total particulates
155
79
134
137
304
781
564
411
559
94
863
126
(125)
178
204
35
87
111
118
70
57
43
50
497
164
123
106
53
99
83
<^22)
778
1600
531
330
134
(125)
173
248
242
244
75
116
15
63
81
49
138
120
80
83
72
98
189
V^ £t£^j
277
1514
649
51
156
(125)
221
109
152
294
CH2
209
, 31
61
33
110
95
122
70
^09)
63
85
147
(222)
69
399
156
96
89
<£H>
70
100
94
178
>d£)
63
71
59
38
63
146
94
166
210
335
143
(222)
53
266
150
139
58
(125)
89
143
185
184
cn>
92
55
27
95
103
/ 3
139
105
109
251
133
174
(22$)
117
80
75
120
98
(125)
95
(Hi)
312
606
CH}
138
53
32
39
37
105
109
<53)
(109)
300
288
282
(222)
(284)
33
60 •
58
-------
TABLE 13. (continued)
Sampler
Mine. Visit Location
3 11
2
3
4
2 6
7
8
9"
3 5
6
7
8
4 11
2
3
4
2 1
2
.: 3
4
3 1
2
3
4
, *
77
130
108
71
171
101
1337
(252)
71
32
50
39
222
78
111
71
387
116
80
308
58
78
82
136
87
95
122
142
211
273
180
(252)
40
70
288
44
92
77
130
142
137
108
150
316
71
77
82
129
Total
99
94
140
101
103
178
772
51
39
13
28
97
97
133
101
82
45
69
218
119
38
97
. 83
particulates (,ag/m )
146
255
111
129
103
119
163
(252)
258
17
204
59
214
105
230
129
104
94
82
62
18
65
164
122
149
88
140
129
121
215
396
(252)
25
17
59
110
145
98
123
119
64
314
181'
116
92
96
105
163
243
77
143
•(114)
101
115
221
(HI)
65
47
29
20
87
263
(lO)
85
262
111
(504)
70
95
218
127
182
97
230
133
88
192
(^5^)
65
33
68
36
96
153
-------
ANOVA: BEGINNINGS OF A SEARCH FOR STRUCTURE
For purposes of the initial exploratory analysis, data taken
during different sampling intervals are considered replicate
diminutions. In fact! such data reflect the error of sampling
rSpficaSons together with whatever systematic effects are introduced
by differences in operating conditions (e.g., truck traffic on haul
roads, dragline and loader operations, etc.) over the intervals in
which data was collected. As a consequence, this simplification may
overstate the variability over replicates and 'bury' real trends or
differences in the 'noise' of replicates. However, any differences
?hatare Sadistically significant given this assumption will indeed
be significant given a smaller mean square error that might result
when systematic effects are removed from the error residual.
Subsequent analyses will attempt to remove systematic effects from the
error estimates and unearth the "fine structure" in the data.
Given the assumption that data from different sampling intervals
at each mine visit are replicates, the experimental design^can be
characterized in technical terms as a mixed 'crossed' and nested
design. (Such a characterization is important because it defines the
requisite computational schemes and tests of significance.) Sander
locations, for example, are nested within mines and season. This is
because the actual placement of samplers varied from trip to trip
depending upon wind conditions, mine activity, and other factors. The
sSSer location numbers 1 through 4 differentiate between locations
for that visit at that mine: they do not imply, for example, that
location 1 at any mine visit will be any more closely related to the
Jl Sampler at any other visit than to the #2 sampler, hence locations
are nested. Seasons (visits) and mines, on the other hand form a
crossed classification. The conventional mathematical model
corresponding to this design and assumptions is then:
*ijka /- *i '3 -13
where M = overall mean
t , i=1 nSandrj M=l a) ~ main effects of mines and seasons
C^i x,p; fj V.J ,HV respectively (note ju defined so that
and
B. • = the interaction effect of mines and seasons,
= -the random effects between the sampler locations
obtained from the 'cell' ij and,
e . . ., ' = random error term.
~a( 1-3 k) ~~^-- -•--=•••
33
-------
In words the above equation assumes that the observed level of
particulates over each sampling interval at each visit to each mine >
(y- -v,v) can be represented as an additive linear combination of an
overall constant ( fj. ) plus a contribution due to each mine (j- ± ), each
visit (a surrogate for seasonality TJ .), an interaction term (fly) to
capture the possibility, for example3, that the seasonal effects might
differ among mines, and a sampler effect (Ak(ij) ) to reflect
differences in sampler placement. Finally, an-error term, 6^(1 jk) Is
included to reflect measurement and other sources of variability not
included in the model (one example would be variations in activity
levels among mines). Subsequent computations will estimate the
magnitude of each of these effects and compare these estimates to the
size of the error term - in this way the statistical significance of
the effects can be adjudged.
Bennett and Franklin (op. cit.) among others details the *
requisite equations and sequence of computations for the analysis of
variance. For this purpose it is necessary to partition the overall
variability, measured as the sum of squares about the overall mean
denoted by S, into its components. Accordingly, the breakdown of the
sum of squares which provides variance estimates corresponding to
these terms is,
s - s. 4- s. + s... + sk(ij) + sa(ijk),
an equation which parallels eq(2) in concept and notation. The
analysis of variance has been computed under two sets of assumptions:
(A) mines and locations are viewed as being a random (or
at least representative) sample, while seasons are
regarded as fixed — i.e., estimates are for these
three seasons only.
(B) as above, except that seasons are also viewed as
random effects.
Assumption (A) appears more nearly correct and will be emphasized in
the following discussion of results. The formal analysis of variance
together with the average values of the variance estimates is given in
Tables 14 and 15 for each of the assumptions. As can be seen, these
differ only with respect to the expected value of the mean square (and
hence in the appropriate F statistics for significance testing). The
algorithm for computation of expected value of the mean square can be
found in Bennett & Franklin (op. cit. pp. 414 et seq.) and is not
repeated here.
' \
For the reader interested in following or checking the details of
.computation, two intermediate tables are presented: Tables 16 and 17.
Table 16 contains appropriate sums and subtotals of data elements
-necessary for analysis of variance computations. Table 17 details
: appropriate formulae and computational results for necessary jsuios o:f
- "•" : ;:: 34 :0: ... .
-------
I
35
-------
tH
O
P 0)
rH H
td td
0) 35
Oi
id C
5-1 (d
||
«
O €
tn T3
0) (U
0) 0)
tn
i i
•H
CO
Mines X seasons
interaction
CM
to
G
CM
to
*-
•
M
D1
•n
•H
co^
Between locations
(within mines S
seasons)
CM
to
i
G
M
O1
•n
•H
cos
m
Between replicate
(within location,
mines and season)
^
|
G
ft
CO
s
36
-------
TABLE 16. ANCILLARY TABLES - SUMS OF RAW DATA
A: Sums over replicates T,
Mine '
Secison
Sampler*
1
2
3
4
1
1
1275
793
796
762
2
1253
1819
1582
1554
3
1989
4451
1715
1657
1
1
771
875
936
1126
2
1169
1666
651
900
3
316
348
428
478
1
1
983
836
994
800
2
943
1089
3261
1764
3
575
255
711
336
1
1078
638
1143
786
1
2
956
1121
842
1428
3
524
593
869
945
* Refers to 1st four samplers for that mine and visit in numerical order. .
B: Sums over locations T.
Mine
Season 1
Season 2
Season 3
1
3626
6208
9812
2
3708
4386
1570
3
3613
7057
1877
4
3645
4347
2931
Cs Typical element T^ ,
Season
Subtotal
1
14592
2
21998
3
16190
Ds Typical element 1^,,
Mine
Subtotal
1
19646
2
9664
3
12547
4
10923
37
-------
§
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u
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rH 0
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o o
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sqTmres"computaticms. FTnally," Tables "18 and "19 'show the "analysis of
variance tableau for assumptions A and B respectively.
Reference to Table 18 (un transformed ANOVA) enables the following
tentative conclusions to be drawn:
— (1) There is significantly greater variability between
; __________ sampler locations (within mines and seasons) than
between replicates (within location, mine, and
season). The computed test statistic or F ratio is
1.66, a quantity which exceeds the critical value
necessary to demonstrate significance at the 95%
(.05) level given the sample sizes. The estimate of
the standard deviation within replicates (as shown
in Table 20) is 162.1. The additional variability
between locations, a^, is 49.9.
(2) There are significant interaction effects between
mine and season. The computed F ratio, 4.21,
. _ ___ exceeds the -threshold level • at -the 95% level.
Estimated values of these interaction effects will
be presented later in this section. However, (ref.
Table 20), the components of variance estimates o^
as 70.8.
(3) Based upon the assumption that data from separate
sampling intervals are replicate values, there is
not sufficient evidence to assert that seasonal
differences are significant (subsequent analyses
will qualify or modify this conclusion) . The
computed F ratio falls beneath the critical level
and, indeed, the components of variance analysis
resulted in a computed value for o^ that was
negative and set equal to zero.
(4) There are significant differences among mines. The
computed F statistic, 5.41, exceeds the critical
value required to assert significance. The
components of variance analysis shows a^ - 47.9, of
comparable magnitude to the other significant
effects and about 30% of the standard error among
replicates.
. ;
NUMERICAL ESTIMATES OF MAIN EFFECTS AND INTERACTION TERMS
Table 22 shows appropriate computational formulae and numerical
estimates for the various terms in the model,
The overall mean value for particulates , /* , is estimated as 157.1
Cue /in 3). The significant main effects due to mines, f i, are shown
n<§xt. Mine 1 had:helarge
n<§xt.
• :" '.' ;-":;: 39
-------
H
,
o
•H
ll
11
g CO
U fa
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CO . C
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Source of
e sbimate
•
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m
CN
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CO
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f<
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CM
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t^
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mines: X season
i
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CN
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CN
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ro
vo
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(within mines fi
seasons)
CN
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CN
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CM
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CN
M.
CTt
rH
in
rH
in
fe
VO
co
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Between replica
(within locatio
mines & seasons
CN!
CO
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CN
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CO -H
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3 • cu
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td id
> 3
CU Dl
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cu o
CU M-4 *UJ
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M-l
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estimate
en
CM
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CM
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to
•«*
00
CM CO.
to
00
CM
J.
CM
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CM
to
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r-
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CN
en
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en
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rH
a
CQ
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4J
r«
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CQ
X!
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fle
e
CO
rH
CO
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•H rH
•P
fti Lrt
> O
P
CU O
CQ
A -P
o rd
M CU
c o
•H C
CQ CU
CQ k
•H CU
? ^
CM 1 -rH
d-l T3
U -H
o
cu
rH
f
•P
I ®
sT'tt
f -r-,
Gl ^
cant
CU tH
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CQ
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CU
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cu
< Q
[:
ii:
41
-------
TABLE 20. COMPONENTS OF VARIANCE ESTIMATES:
MINES AND LOCATIONS RANDOM, SEASONS.FIXED
Term
a
°A
°0
°r,
°S
Estimate
r 2 "]i/2
If « (ijk)J
r 2 2 "11/2
sk(ii)~s«(ijk)
L n J
T 2 2 "11/2
s irs kcij)
L nr J
r 2 2 Ti/2
Si - S ij
L prn J
r 2 2 11/2
Si - S k(ij)
L qrn J
Numerical value
162.1
49.9
70.8
I/
0
47.9
^Computed quantity was negative so set equal to zero,
42
-------
TABLE 21. COMPONENTS OF VARIANCE ESTIMATES:
MINES, SEASONS AND LOCATIONS RANDOM
F 2 2 "I I/2
sk(ii)"S<*(ijk)J
^Computed quantity was negative so set equal to zero,
43
-------
TABLE 22. NUMERICAL ESTIMATES OF MAIN EFFECTS AND INTERACTION TERMS
Effect
overall
mean
main effect
Mines ^ *
main effect
Seasons
_
3
interactions
Mines X Sea-
sons
B- • *
13
E stimate
y
%..-y
_ —
• j "
y. . -y, -y . +y
13 • l- • • D •
Numerical value (
, 3
/U9/m )
yU= 157.1
£x - 233.9_-y =
£3 = 149.4-y =
£„ m 130-y
ni = 130.3-^ -
^ 2 = 196.4-y_ =
n3 = 144. 6-y =
Bn = -77-6
Bl2 = ~51-5
'Bis = 129.0
$ zl s= 44.2
B 22 = 2'3
B 23 = -46.4
Bai = 6-4
B32= 63-3
B33 = ~69-9
34l « 27.0
B,3 = -12.8
76.8
-42.1
-7.7
-27. 0
-26.8
39.3
-12.5
*These differences are statistically significant.
44
-------
us/in5 5""hence a value +7678" /Tg/m'3 higher than the overall mean while
mine 2 had the smallest (11.50 /zg/m3). As will be discussed later,
these differences can partially be explained by differences in
activity patterns.
As highlighted in point 3 above, seasonal effects cannot be held
to be significant. This result is somewhat surprising as, at least in
winter,-when much-of-the ground is snow-covered, particulates might be
expected to be lower. All four mines had snow cover in the early
winter visit. The main effect for this season, rj 3, is indeed negative
12.5 Mg/m3 less than the overall mean, but in view of the overall
variability this is a small effect. More puzzling is the size and
magnitude of the main effect for season 1, -26.8. It is often assumed
that particulates are more of a problem in the summer, an observation
of variance with this data. The following analysis potentially
resolves this contradiction. . . . .
Interaction effects for each mine and season, denoted by ^ij (:L =
1, 4i j = 1, 3) are significantly different from zero. Some of the
largest interaction effects are associated with mine 1, with lower
than expected (on the basis of main effects alone) particulates in
seasons 1 and 2 and higher than expected particulates in season 3.
Mines 2 and 3 each had substantial negative interaction terms for
season 3 (early winter).
The presence of significant interaction effects serves both to
complicate the model and make interpretation of results more
difficult. The section following, entitled "The Choice of a
Transformation" examines the use of power law transformations to
achieve model additivity (absence of interaction) as well as other
statistical properties. The balance of this section explores a
somewhat more speculative approach to the examination of interaction
effects.
In the context of this experimental design, interaction effects
are measuring the (possibly unique) circumstances at each visit at
each mine. Literally these effects are defined by the difference in
response between what can be accounted for by whatever main effects
exist and the observed particulates for that visit. Thus, for
example, in the_absence of^interaction, the expected mean particulates
'for visit 3 to mine 1 is the overall mean, 157.1 /ig/m3, plus the main
effect due to mine 2, 76.8 Mg/m3 (see Table 22) plus the main effect
due to season 3, -12.5 Mg/m3 (see Table 22) or 221.4 Mg/m3. The
observed mean value (see Table 16) is 9812/28 = 350.4 Mg/m3. The
interaction term, /313, is then 350.4 - 221.4 = 129.0 Aig/m* as shown in
Table 22.' /313is, in fact, the largest of the computed interactions.
It is tempting to see if any assignable cause can be identified to
"explain" this interaction. If one can be found and a more reasonable
estimate of the response substituted for the observed value (350.4
Mg/m3), then recomputation of the main effects may lead to additional
insights. Though the search for an explanation may have been
initiated by the presence of a large interaction, the burden of proof
for adjustment of cell values must..be based upon physical rather than
statistical grounds^, j _ "! ' 45 •'•^
-------
: A~detailed examinaTion of"the data recorded in visit 3 to mine 1
su'ggests one anamoly - dragline activity during this visit was an
order of magnitude greater than at the other mines during this visit.
It is reasonable to expect (and later shown to be true) that dragline
activity is associated with increased emissions and hence increased
particulates. Thus a possible explanation for this observed
interaction is the higher than normal activity. If this is true, then
for -purposes of"the ANOVA-(evaluation of main-and interaction effects)
it is appropriate to make some adjustment to the observed response and
recompute the main effects and interaction terms.
The most common procedure for such cell adjustment (see for
example a recent article by Daniel, Technometries Vol. 20, #4,
November, 1978) is to regard the anamolous response as missing data..
An estimate of the value to assign to the cell is then obtained so as
to minimize the sum of squares of interaction terms. Table 23
following shows the mean values, and row and column means for all
visits to all mines. An unknown value, 713, is assigned to the
response to be estimated.
Following the usual partitioning into the components of the sum
of squares for the ANOVA, we obtain:
2
(a) Correction term: C = 1/12 (1534.5 + y±3) (4)
(b) Between Mines:
1/3 [(351.2 + y13)2+ 345.12 + 448. 2 + 390.22] - C (5)
(c) Between Seasons:
1/4 [521.12 + 785.6 2 + (227.8 + y±3)2 ] - C (6)
(d) Between Observations:
[i29.52+ 221.7 2+ y 2 + . . . + 104.72] - C (7)
,1 •*>
The interaction (or residual) component is computed as (7) - (6) -
(5), and, neglecting purely numerical terms which will disappear on
differentiation, the sum of squares, S, becomes:
2 + . . . + 1/12 (y132+ 30,690 y13 + . . .) (8)
- 1/3 (y132+ 70,240 y±3 + - - •)
- 1/4'(y- 2+ 45,560
y!3
Obtaining the partial derivative ofA(8) with respect to y-i3, setting
this equal to zero and solving for y13 yields the value 92.2. In
other words 92.2 is the adjusted cell mean for particulates in visit 3
to mine 1: an estimate of the particulates that would have been
obtained in the absence of the unique conditions of this visit.
46
-------
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47
-------
This estimate, 713, in turn can be used (in conjunction with the
other values shown in Table 23) for recomputation of main and
interaction effects under this "what if" hypothesis. These
computations are summarized in Table 24. Results of this speculation
are interesting. The seasonal effects are now more pronounced and, in
particular, the seasonal effect corresponding to winter conditions,
-55.6, is negative and of appreciable magnitude. The magnitude of the
contraintuitive seasonal effect for the summer season, -26.8^g/m3, has
been reduced to 80% to -5.3jug/m3, a happy consequence. Note also that
the other interaction effects 0^ are appreciably smaller than in the
original analysis shown in Table 22 - the root mean square interaction
term dropped by nearly 60% from 59.9 to 24.8. Such results are
certainly plausible if not definitive.
In the following section we explore yet other approaches for
sharpening the results of the ANOVA.
THE CHOICE OF A TRANSFORMATION
- —There are several assumptions which underlie the ANOVA procedure.
Of particular relevance in this context are the assumptions of
homogeneity of variance and normality of residuals.
If the variance of the random components in an analysis differs
from cell to cell, the ANOVA procedure leads to a loss in efficiency
in the estimation of effects and (possibly) a distortion of the
significance level of comparisons. This is because all observations
are weighted equally in deriving the estimates of effects, a procedxire
which is inappropriate if observations have different errors. The
significance level of the results may be distorted because a common
variance estimate is used in examining all comparisons rather than a
series of different variance estimates.
If the residuals do not satisfy the distribution assumptions
(i.e. normally distributed) upon which the test procedures are based,
then the statistical significance of the results may be misstated. In
practice, however, even substantial deviations from normality appear
not to affect the results greatly — the ANOVA procedure is said to be
robust.
An examination of the residuals from the predicted values of TSP
at each mines season, and location indicates that:
(1) the residuals do not appear to be normally
distributed, but rather come from some 'long tail1
distribution (e.g. gamma, Weibull, log-normal, etc.)
(2) the residual variance is not constant from cell to
cell and appears to be an increasing function of
cell mean and
(3) there may be some outliers present 33
48
-------
TABLE 24. CONSEQUENCES OF DELETING DATA FROM MINE 1 SEASON 3
(A) Main-effects UNTRANSFORMED DATA - ALL UNITS IN fJ.q/m
I
Season 2
3
y±.
*i
Mine
1
129.5
221.7
92.2
147.8
12.2
2
132.4
156.6
56.1
115.0
- 20.6
3
129.0
252.0
67.0
149.3
13.7
4
130.2
155.3
104.7
130.1
- 5-.5
7fj
130.3
196.4
80.0
M =135.6
"J
- 5.3
60.8
-55.6
*Estimated value
(E) Interaction and/or error terns
1
Season 2
3
Mine
1
- 13.1
13.1
0
2
22.7
- 19.2
- 3.3
3
- 15.0
41.9
- 26.7
4
5.4
- 35.6
30.2
NOTE: The seasons correspond to the three visits. Their dates appear
on page 2.
49
-------
The first two points above suggest some transformation be applied to
the data in an attempt to remove the heteroscedasticity and apparent
non-normality. In a seminal paper in 1964, referenced earlier, Box
and Cox developed a constructive algorithm for choosing a
transformation. The parametric family of tranformations considered by
Box and Cox are of the form:
- i
e
•i
In y
6 ? 0
e = o
(9)
The above transformation holds for y > 0. For a value of 6 = 0, (9)
is a logarithmic transformation, for 8 = -1, (9) is a reciprocal
transformation, while for 8=1, (9) leaves the data unaltered.
REVIEW OF RELEVANT THEORY
:- —A -brief sketch of the main ideas in the Box and Cox paper
follows:
are assumed to be generated
(1) The data yi ... yn »j.*= a.&aw^^ «-^
by a linear model of the form E [y (#)] = aw, where
is a known matrix, 0 is as defined above and w a
vector of unknown parameters associated with the
transformed observations.
(2) For some unknown 0 , the transformed observations y
assumed to satisfy full normal theory assumptions x
independently normally distributed with constant
variance a2 and expectation E[y(0)] = aw. The
probability density function (pdf) for the
untransformed observations and the likelihood in
relation to the original observations is obtained by
multiplying the normal density by the Jacobian of
the transformation as
(0)
are
exp
(y
(0)
- aw)1 (y
(0)
- aw)
J(0;y),
n
where J(0 ;y) = II
dy.
(0)
(10)
(3) The above likelihood (10) is, except for a constant
factor, the likelihood for a standard least squares
problem. Hence the maximum likelihood estimates for
the dependent variable y(^) and the estimate of a2,
denoted for a fixed 8 by£2(0) is
50
-------
- a(a'a)"1a')y(5)/n =
More simply, if the normalized transformation
is used then the maximized log likelihood is given
by
and
where S(0:z) is the residual sum of squares of z v .
____ For the power transformation
f 0 } v ^ — 1 •
z = — S — T where y is the geometric mean
0y of the data
(4) The best value of 8 to choose under the assumption of an
additive, homoscedastic and normal model in the
transformed observations can be found by minimizing the
residual sum of squares (in an additive model, estimated
by the pooled residual and interaction sum of squares,
that is S^-j + Sa(ji]£)in the notation of the foregoing) of
the normalized z values as a function of 0 . The maximized
log -likelihood under the assumptions of additivity,
homoscedasticity and normality, denoted by
Lmax (*1A>H»N) is given by,
Lmax CA,H,N) = -1/2 n In (11)
(5) The best value of 9 to choose under the less restrictive
assumptions of homoscedasticity and normality can be found
by the procedure above where the criterion function is the
residual sum of squares. The maximized log-likelihood
under the assumptions of homoscedasticity and normality,
denoted toy
Lmax <*1H>N> is g±ven bv»
51
-------
L (6 H.N) + -1/2 n In
max '
where Sa (0 :Z) = sa/jMk) in the ANOVA notation
APPLICATION TO PARTICULATES DATA
Table 25 and Figure 2 show results of numerical optimization of
the log-likelihoods Lmax(0lA, H, N) and Lmax(0lH, N) respectively.
On the basis of either criterion, the optimal value of 6 , B is about
-0.1 — that is, approximately equal to the value 8 =0.0 which
corresponds to a logarithmic transformation of the data. Indeed, an
approximate 100 (1 - a) percent confidence region for 9 can be found
from
v2 (a) _ __ _ (13)
~'fl — —.
where YQ is the number of independent components (in this case one)
in 6 . A. 99.5% confidence interval, therefore, includes all 6 values
with log-likelihood within (7.88/2) of the minimum value. Thus, the
value 0 is included in this confidence interval for the additive,
homoscedastic normal model.
The logarithmic transformation is intuitively plausible and has
been shown above to be an appropriate model for particulates data. It
will be employed henceforth in the analysis procedure.
Table 26 details appropriate formulae and computational results
for necessary sums of squares computations with the logarithmically
transformed data. It corresponds to Table 17 for the untransformed
data. Finally, Table 27 shows the analysis of variance tableau on the
assumption of fixed seasonal effects.
Interestingly, the conclusions that emerge from this analysis are
identical with those reported for the untransformed data, viz;
(1) a significant main effect for mines,
(2) no proven significant effect for seasons,
(3) a signficant interaction effect between mines and
seasons. Unfortunately, the "best" transformation
failed to achieve additivity in this instance and
finally,
(4) significant differences among locations within mines
and seasons.
52
-------
TABLE 25. NUMERICAL SEARCH TO SELECT OPTIMAL TRANSFORMATION
Transformation
name
Square .
Ontransfonced
Squars root
Logarithmic
Reciprocal
9
2.0
1.0
0.5
0.4
0.3
0.2
0.1
0.0
-0.05
-OJ.
-0.2
-0.3
-0.4
-0.5
-1.0
-2.0
S(9i z)
2,365,278.25
76,191.62
26,000.62
22,510.81
19,986.93
18,196.75
16,992.69
16,277.13
15,984.69
15,984.69
16,100.12
16,632.37
17,599.63
' 19,085.37
39,459.00
514,192.00
L C^M,!*)^
ntcuc
-1,488.36
- 911.21
- 730.59
- 706.38
- 686.40
- 670.64
- 659.14
- 651.91
- 649.90
- 648 .86
- 650.07
- 655.54
- 665.03
- 678 .65
- 800.67
-1,231.98
Sf(9', z)
2,156,767.00
65,147.25
21,175.00
18,152.31
15,961.37
14,399.94
13,333.69
12,676.44
12,482.50
12,374.00
12,406.12
12,774.00
13,507.19
14,657.87
31,397.00
461,328.00
L (d\X,X1-
max
-1,472.86
- 884.91
- 696 .10
- 670.23
- 648.62
- 631.21
- 618 .40
- 609 .91
- 607 .32
- 605.85
- 606.28
-. 611.19
- 620 .57
- 634.30
- 762.28
-1,213.76
All TS? values were scaled by a factor 0.1 in preparing this table. Such a.
scalar transformation will not altar the optimal functional trar.sfcrna-icn.
I/
2/
^ __
• „ jji
336 ,
53
-------
-500
-600
-700
-800
-900
-1.0
Figure 2. Shape of likelihood surface for selecting transformation.
(Data plotted from Table 25.)
54
-------
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56
-------
A"BETDBN "TO'THE"QUESTION 'OF"OUTLIERS "
Earlier in the discussion the possibility of outliers in the data
was raised. An examination of the untransformed data, see Table 13,
suggests as many as three outliers in the data. These values are:
Mine
1
1
3
Season
3
3
2
Location
2
2
3
Replicate #
2
3
1
Numerical
Value
1600
1514
1337
The justification for suspicion of these values are the computed values
of the standardized residuals. For example, the row mean for total
particulates in mine 1, season 3 and location 2 is 635.9 jug/m3
(including the candidate outliers 1600 yug/m3 and 1514 /ug/m3). From
Table 18, the estimated residual standard error is 26,271 = 162.1.
The -value 1600 is thus (1600 - 635.9)/162 = 5.95 multiples of the
standard error above the row mean. Similar claims can be made for the
other candidates.
However, the case for these values as outliers becomes
significantly less compelling when the logarithmic transformation is
employed. For example, shown below are the replicates in both natural
and transformed units:
Natural units, y
Transformed units
y In y = z
559
740.1
1600
863.12
1514
856.66
399
700.65
266
653.21
80
512.65
33
409.06
The row mean for the transformed values is 676.49. From Table 28 the
standard error among residuals is V5109 = 71.48. The standardized
residual for the value 1600 in transformed units is (863.12 -
676.49/71.48 = 2.61, much lower than was the case for the untransformed
values. (Indeed, the value 33 now has a higher standardized residual.)
Similar conclusions follow for the other candidate outliers.
Fortunately, however, the conclusions are insensitive to the
outlier question. Replicate analyses on the transformed y values with
these candidate outliers deleted (and replaced with corresponding row
means 269, 269, 321) results in conclusions which are substantially in
57
-------
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1
1
1
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"3
a!
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we W-H WT-,
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58
-------
agreement"with the "ANOVA "including these"values.~ Tables 28 and 29 show
the supporting computations. The only difference of note is the
reduction of the F value for the test of differences among locations —
a difference can only be asserted at a lower (but still appreciable)
level of significance.
Thus the conclusions do not turn on the question of outliers, so
this.issue is-for practical purposes, irrelevant. Nonetheless, it was
important that this was done and the conclusion reached.
This completes the preliminary data analysis. Succeeding sections
investigate further the findings and hypotheses suggested by this
preliminary analysis. In brief, this further analysis
(1) explores the differences in particulates among
samplers at various locations, and,
(2) examines the relationships between observed
particulates and activity levels, wind speed and
.other .variables at ...various mine, visits.
DIFFERENCES AMONG SAMPLER LOCATIONS
The results of the analysis of variance support the contention
that there were significant differences among sampler locations. This
finding is expected on the basis of physical theory - different
samplers "see" different emissions inter alia based upon their
location relative to sources and the wind direction and speed. In
particular, a significant contrast between the "upwind" or ambient
sampler and the downwind sampler(s) is anticipated.
To test this hypothesis the data sets of particulates, time
sequence, weather conditions and locations were merged and a subset of
the observations selected for which clear upwind and downwind samplers
existed. Two sets of definitions were used in generating the data
sets.
The most restrictive criteria included:
(1) Time coincidence - the sampling periods of each of
the three or four samplers must be nearly coincident
in time. The "time window" chosen was one hour.
That is all paired observations had start times
within one hour of each other.
(2) Location relative to mining activities and wind
direction - Each sampler was assigned a
corresponding ambient sampler if for every hour of
sampling one sampler was upwind of all mining
activities.
59
-------
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. . (3) "Commonality "of ambient sampler - A block of three or
four time coincident samplers was accepted if all
! members of the block had the same corresponding
-••:". ambient sampler.
>; Since roughly seven samples (approximately one per shift) were taken
';! : at each location on each visit to each mine, the possible number of
"' ." data blocks is 7 x 3 x 4 = 84. If the wind direction were such that
there was one ambient sampler, no wind shifts, and data collection
1 always satisfied the time coincidence constraints, 84 data blocks
1; . would have resulted. Wind shifts and other factors combined to reduce
the number of data points (blocks) that satisfied the above criteria
to 21. These are shown in Table 30 and are referred to as "data set
1". . --- -
In view of the relatively small number of data points which
satisfied the set of criteria, another less stringent set of criteria
was employed. This less restrictive set of criteria differed from the
first set in that
.(!)_ each sampler was .assigned as a corresponding ambient
sampler one of the samplers most frequently upwind
during the sampling period. No corresponding
ambient sampler was assigned, however, if the wind
was calm throughout the sampling period or if the
modal condition had no sampler ambient. The time
coincidence constraint was relaxed from one hour to
0.1 day or 2.4 hours.
The less restrictive rule did permit sampling hours when no sampler
was ambient or the wind was variable. To illustrate the differences
between the criteria, consider the following examples:
Example 1
Mine 1, Trip 1 - Between 25.96 (earliest start time: decimal
day) and 26.33 (latest end time: decimal day) the
following observations were recorded:
Sampler Location # Total Particulates
1 95 Mg/m3
2 122 ,ug/m3
3 80 jug/m3
*
4 106 Atg/m3
For every hour in the interval, sampler location 2
was upwind of all mining activities. This block
satisfies the more restrictive criteria and is
'shown as data point 1 on Table 30.
: 61 ;
r
-------
TABLE 30. UPWIND AND DOWNWIND SAMPLER ANALYSIS
DATA SET 1 .
Observation
1
2
3
1+
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
•21
Mine
1
1
1
1
1
1 •
1
2
2
2
•2
2
2
2
2
3
3
3
3
3
4
Season
1
2
2
. 2
3
3
3
1
1
1
2
2
2
3
3
2
2
2
2
3
3
TSP for
upwind
sampler
122
781
72
its
778
277
59
131*
100
143
209
83
232
32
49
171
103-
121
133
10
50
TSP for downwind
samoler /J/3/m^
Dl
95
304
98
282
1514
399
266 '
125
58
110
185
606
205
95
66
101
178
119
215
17
83
D2
80
564
189
204
649
156
150
178
95
35
184
312
164
37
39
1337
772
163
221
68
183
D3
106
-
-
-
863
330
51
204
—
-
-
-
S3
21
-
-
-
9
90
TABLE 31. PAIRED t TEST ON UPWIND VERSUS DOWNWIND SAMPLER LOCATIONS
DATA SET 1 (TRANSFORMED OBSERVATIONS)
Observation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
IS
16
17
18
19
20
21
uu
2.501
4.358
1.974
2.674
4.354
3.321
1.932
2.595
2.303
2.660
3.040
1.841
3.144
1.163
1.1589
2.839
2.332
2.493
2.588
0
1.609
ud
2.231-
3.723
2.611
3.177
4.550
3.310
2.539
2.807
2.005
1.825
2.915
3.772
2.909
1.742
1.330
3.604
3.613
2.634
3.082
.781
2.407
wa-uu°di
- .270
- .635
.637
.503
.196
- .011
.607
.212
- .298
- .835
- .125
1.931
- .235
.579
- .259
.765
1.281
.141
.494
.781
.798
62
-------
Example 2
Mine 3, Trip 3 - Between 19.38 (as above) and 19.99 (as above)
the following observations were recorded:
Sampler Location # Total Particulates
5 65
6 33 /zg/m3
7 204 Mg/m3
8 110
During this time period the following conditions
occurred on an hourly basis:
__________ Condition ._ . - ...... Fraction. of Hours
#6 upwind .59
#8 upwind .06
none upwind .06
wind variable .29
This block satisfies the less restrictive criteria
since sampler #6 was most frequently upwind.
The set of points that satisfy the less stringent criteria
include, of course, all of those satisfying the more restrictive
criteria, but also permit additional points to be included. These
additional points are shown in Table 32 and are referred to as "data
set 2". Collectively, data sets 1 and 2 include about half the
observations.
Turning now to the question of analysis of results, note first
from either data set that, perhaps contrary to expectation, the upwind
or ambient sampler is often higher in particulates than some or even
all of the downwind samplers. Referring to Table 30, for example, by-
actual count the ambient sampler had the largest total particulates in
6 cases (29%) and was higher than at least one of the downwind
samplers in 14 cases (67%). Similar results were obtained for data
set 2. This data shows that variability in so-called background or
ambient TSP levels are highly variable and often large in relation to
mine-induced emissions. (No implication should be drawn that the mine
is 'removing particulates from the air,1 though indeed some settling
may occur. Rather, the inference is drawn that substantial temporal
variability and/or measurement errors exist.) For this reason the
' ' ' 63
-------
TABLE 32. UPWIND AND DOWNWIND SAMPLER ANALYSIS
DATA SET 2
Observation
1
2
3
i+
5
6
7
a
9
10
11
12
13
It*
15
16
17
18
19
20
21
22
Mine
1
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
3
4
4
i+
t
Season
1
2
2
3
3
3
1
1
1
2
2
3
2
2
2
3
3
3
1
1
3
3
TSP for
unwind
sampler
197
781
til
S3
77
38
125
134
143
118
92
71
211
121
101
32
28
33
222
92
70
SO
TSP for downwind
sampler #g/m3
Dl
164
304
531
75
120
58
-
178
98
294
73
27
273
119
-
72
288
65
_
_
163
185
D2
123
564
1600
80
60
36
-
125
95
152
149
38
180
163
396
50
258
110
71
97
95
121
D3
_
-
-
51
106
55
—
204
-
-
'-
103
-
-
-
39
17
204
111 •
230
218
144
TABLE 33. PAIRED t TEST ON UPWIND VERSUS DOWNWIND SAMPLER LOCATIONS
DATA 'SET 2 (TRANSFORMED OBSERVATIONS)
II
ol
Observation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
IS
16
17
18
19
20
21
22
Uu
3.906
4.358
3.716
1.668
2.041
1.335
2.595
2.660
2.468
3.219
1.960
3.049
2.493
2.313
1.153
1.030
1.194
3.100
2.219
1.946
1.609
"d
2.653
3.723
4.524
1.908
2.213
1.581
2.807
2.267
3.051
2.345
1.553
3.099
2.634
3.679
1.644
2.380
2.428
2.184
2.704
2.708
2.693
"d-wus4l
-1.253
- .635
.808
.240
.172
.246
.212
- .393
.583
.126
- .407
.050
.141
1.366
.481
1.350
1.234
- .916
.485
.762
1.084
64
-------
quest ion "of "the ..... relative "contribution of mining" operations to TSP
levels is ultimately a statistical one: what differences exist, on
average, between ambient and downward locations?
From a statistical point of view there are several possible
contrasts which can be tested. One reasonable comparison is the
contrast between the TSP of the ambient (upwind) samples and the
average of the downwind samplers. Under the null hypothesis that
there are no differences between the ambient and downwind samplers,
this difference should equal zero.
For reasons advanced earlier in this chapter it is appropriate to
use the log transform of the particulates data. Table 31 showed the
natural logs of TSP values for the upwind and average (of the
logarithms of the) downwind samplers. Since the logs transform
induces approximate normality to the TSP values (and the central limit
theorem fills in the balance), the paired "t" test is appropriate.
The log of the differences A j_ for each observation is shown also in
Table 31. The computed mean and standard deviation of the values for
data set ..1. are. 0.298 and 0.650 respectively. . The computed t value is,
0.298
t ~
0.650/ V21 ~
a value which is statistically significant at the a = 0.05 level.
Thus it can be asserted that the average of the downwind TSPs exceeds
the ambient TSP. Based upon data set 1, the mean difference, 0.298,
represents a factor exp (0.298) or 1.35. That is, the mean downwind
TSP is some 35% higher than ambient conditions.
Similar conclusions emerge if data set 2 is considered.
Statistical tests indicate that the observations from data sets 1 and
2 can be pooled, that is the data behave as though they come from the
same population (F test on homogeneity of variance, t tests on means).
In turn, this pooled data set can be used to compute confidence
intervals for the contrast, as detailed in Table 34 following. Based
upon the pooled data sets, the downwind samplers differ from the
ambient by a mean factor of 1.33 (i.e. 33% higher). The 95%
confidence level interval is from 1.079 (7.9% higher) to 1.64 (64%
higher) . The difference between downwind and upwind samplers helps to
explain the ANOVA result identified earlier (the significance between
sampler variation). Unfortunately, the sample size of data sets 1 and
2 is too small and not sufficiently evenly distributed to permit an
ANOVA to test for main effects of mine and season.
In view of the relatively large ambient TSP levels (in relation
to downwind values) it is interesting to explore what relationships,
if any, exist between these values and other measured variables.
Three variables plausibly correlated with ambient TSP include wind
speed, soil moisture and snow cover. Ceteris paribus, the following
. : -•- - ;.;: 65 ;: ,
-------
TABLE 34. SUMMARY OF RESULTS FOR UPWIND VERSUS DOWNWIND ANALYSIS:
TRANSFORMED DATA
Data
isett
t computed
Remarks
0.298
0.650
21
2.10
sig-.<§ .05 level
0.273
0.715
,21
1.75
sig.S .10 level'
Pooled
0.286
0.675
42
2.75
si?.3 .05 lavel
95% Confidence liaitts
- a/2) ,
2.021 2*|Z2. - 0.210
Ccnfidencs
or
>
0.236 £ 0.210
[0,076, 0.496]
;Ln untransfarsed
> 1.331
a0'076 - 1.079
1.642
66
-------
relationships nave' been" suggested in the literature or
otherwise expected.
(2> ambient TSP levels are expected to decrease with
U_inoreasing soil moisture and, -•
(3) the effect of wind velocity is somewhat more
complex,
related to wind speed, but
53
increased emissions
to contradxct ^tSSaS- These findings are not
reasons:
The MOVA considered all samplers not only those
that ^asured upwxnd or ambxent cond differences
foregoing Analysis has shown J^ Downwind
between ambient jnd downwind sampler ^.^ ^ ^
samplers should repecX,,r^°;;tivitv levels at the
0^? S^ad SS cover at each mine.
/IN o-hov^ the ANOVA considered samples
-- rocedure ts
taKen ovex «*•.--—-~- - e of this proc«
|r determinations, ^consequence activity
, ^-SST^-Sar siaio^'eSJcS^re
I:' " ::- —SS^^-S^'SSoSS .y the ANOVA.
' - ' •": 67 L: ' - ----- •-
l;i "."T..""" """-^ ; V- -;—- - ' •;-• -;•
-------
1000-j
TSP loc-
Cug/m )
1.0
©
IOO
O SNOW CCVEr?
^ NO SNOW CCVE??
ICO
WIND SPEED (m.p.h.)
Figure 3. Scattergram (log-log scales) of TSP for ambient
samplers: data sets 1 and 2 with outliers deleted.
68
-------
„ c^rtne WTSr-STiS STS^HS^S" "
,: simply te.^s^r distribution (data sets V^^ions and sampler
': SSS^sS^s'^SlJ5T-SSS-^i.^or a11 visits)'
Di
.
f ower.
Power.
stignilioalltly,
cover suggesting ™r_I ion _ though
iSld^Xs^ed"i?S ^dictive power
,, RELATIONSHXP OP TSP TO ACHvm I^VELS ^^ ^
of the mining actiTL^_JLtive, a consequence of samP-Laiikej for which
.
1s:»»'=«?sS1E?.rss'Si« -•-»-
ctivi.ties ne .
activi.ties Jnf^e0Ser studies.
been estimated in other s logarithmic
For reasons discus-* -rljer^the^ yalue . This same
- transformatio dco _..._.,....-.--
-------
TABLE 35. TABLE OF ACTIVITY NUMBERS
DEFINITIONS FOR REGRESSION ANALYSIS
Variable
name
Operation
Units (all per shift)
Ql
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Q9
QlO
Q12
Speed
Precip.
Dragline
Coal haulage
On-mine vehicles
Overburden drilling
Coal drilling
Blasting
Water trucks
Vehicles, public roads
Coal loading
Coal unloading
Scraping
Grading
Wind speed
Precipitation
Operating minutes
Truck trips
Miles traveled
Operating minutes
Operating minutes
Number of holes
Truck trips
Number of vehicles
Tons
Tons
Operating minutes
Operating minutes
M.P.H.
Fraction of sampling period
during which rain occurred.
70
-------
transformation has been applied to each of the dependent variables, so
that multiplicative relationships of the form
Qr
Q,
. Q
n
are being evaluated.- Other functional forms such as polynomials were
also evaluated. However, in general these gave no better fit to the
data and the coefficients of polynomial models are notoriously
difficult to interpret (see Mosteller & Tukey Data Analysis and
Regression, Chapters 12 and 13). The discussion therefore will center
on multiplicative models.
Before proceeding to discuss the regression results in detail it
is important to identify some limitations inherent in the regression
approach. An awareness of these limitations is essential to a clear
understanding of the subsequent data analysis.
..In the.main, these limitations result from the fact that the
experiment was not totally controlled. Some variables, such as the
mines to be visited, the visit times and the placement of samplers
were controllable. Other important variables, however, such as the
level and pattern of mining activity as well as weather were not
controllable. Thus, we were limited to passive observation rather
than active design. Some consequences endemic to observation rather
than design are:
(1) Limited range of observations - It is self-evident
that if an independent variable does not change over
the observation period, then its effect cannot be
determined. Even if it does vary, but only over a
limited range, the effect of the variable may be
masked by the noise of experimental error.
Specifically, it can be shown that the standard
error of a regression coefficient tf(/3i) is of the
form,
n
2.
CQ±
-Q)2
1/2
where
G
o- standard deviation of the measurement error
Q. = values of the independent variable
Q = mean of independent variable = 2 Q./n
x
71
-------
" "Note from the above equation that the precision of
measurement of the effect of a variable (as
Vff (^i))_i§ directly related to the spread (as
(2 (Qi - Q) r ) °r standard deviation of the
independent variable. For this reason, a basic
maxim of experimental design is to "spread out" the
independent variables. Derivatively, the effect of
-variables with small variability cannot be
ascertained with much precision.
Table 36 shows some summary statistics on the mean
values and standard deviations (in transformed
units) of the independent variables. Several of the
variables ((34, QS, and particularly Qy) had small
c(Q) values. Hence the effects of these variables
may not be well determined.
(2) Multicollinearity and shadow variables -
Multicollinearity is a word used to describe a
situation in which one or.more independent variables
"move together" or are correlated. If, for example,
two independent variables, say Q^and Q2, are
perfectly correlated (an exact relation such as
Q2= kQi) then it is impossible to "separate out"
the effects of each on the dependent variable. Even
if the dependence is less than perfect, but still
appreciable, then estimation problems remain. The
confidence statements about the effect of one
variable, for example, must assume a value for. each
of the others.
In a designed experiment, the independent variables
are controlled so as to be orthogonal (i.e.
uncorrelated) or nearly so. In an observed
experiment, this situation may not be obtained.
Table 37 shows simple correlation coefficients
between each pair of independent variables (in
transformed units). Note that several pairs are
highly inter correlated. The variables Q12
(scraping) and Q-i ^(grading) have the highest simple
correlation coefficient, 0.69. Other pairs of
variables which have relatively high correlation
include (please examine Table 35 for definitions of
each variable):
Q3 vs Q2 0.66
Q8 vs Q2 0.65
Ql2vs precip. -0.61
Q10vs Q9 0.62
; __ :' ! 72
I". ; '" "~ '••-"• • " '"._•_-*
-------
[;
TABLE 36. MEANS S STANDARD DEVIATIONS FOR REGRESSION ACTIVITY ANALYSIS
(ALL VARIABLES TRANSFORMED AS NATURAL LOGARITHMS)
Variable
TSP
Speed
Precip.
2l
Q2
23
24
95
97
98
9o
Jiy
9io
9l2
Mean
2.2555
2.0427
-0.0447
4.4982
1.8364
2.9137
0.1221
0.0380
0.6309
0.0078
0.8687
1.3722
0-.6340
0.5814
0.3582
Standard
' deviation
0.8620
0.6919
0.2252
2.3120
1.8816
1.8121
0.4432
0.2234
1.5148
0.0800
1.4469
2.9417
2.0681
1.7428
1.1245
Ratio of 0/M
.38
.34
-5.04
.51
1.02
.62
3.63
5.88
2.40
10.35
1.67
2.14
3 ; 26
3.00
3.14
Cases
212
212
212
212
212
212
212
212
212
212
212
212
212
212
212
For definition of variables see Table 35.
[-
73
-------
o
o
i t
m
H|
tnl
1
•8
Hi
s
a
cnl
H
ol
ol
o
8
8
a
H
tnl
I
•g
8
0^00^00000000.= «=
.
o
I
3 o-ooooooo-oooooo
I
-oooooooooooooo
ooooooooo-o
II II"
I
o -• jj
r1-^—
&
Q) O
o ^ «
74
-------
I:
....... Q 5 vs Q4 ...... 0.55
Q 1:Lvs Qt 0.55
These simple correlation coefficients are of
comparable magnitude to the multiple correlation
coefficient finally obtained through regression
______________ . ...... analysis. - Multicollinearity then may pose problems
for the analysis.
Briefly, the possible consequences of
multicollinearity are difficulties in separating out
effects of variables, lack of precision in
estimation of coefficients and finally sign
reversals in the effects of variables. (We shall
see that this accounts for a sign reversal in one of
the coefficients.)
(3) Issues of uniqueness of estimates - The logic of
using a fitting criterion such as minimizing the sum
of squares of deviations about a fitted model is
clear. It often happens in practice, however, that
equations which appear to differ substantially have
similar predictive power (the residual sum of
squares surface is relatively flat). Best fit
equations may therefore differ substantially from
the true but unknown relationship.
The foregoing discussion of limitations is not
intended as a littany of dispair. Rather, its
objective is to provide necessary background for
data interpretation. Except for unusual
circumstances where field experiments can be fully
controlled (e.g. specifying the pattern, intensity
and duration of each mining activity) , such
limitations are likely to characterize all future
research. The complexities of data analysis which
characterize this research are thus prototypical.
As such, the research reported herein anticipates
what will occur in subsequent analysis - and is
useful if only for this reason.
DETAILS OF REGRESSION RESULTS
The first observation of interest in examining the relationship
between mine activity and resulting TSP values is the pattern of simple
correlation coefficients. These correlation coefficients are shown in
the leftmost column of Table 37. Two points are worth of note: first,
with the exception of variable Q3 (on -mine vehicles), TSP is positively
correlated with each of the activity variables. (The only other
negative correlation is between TSP and precipitation, a relationship
expected on physical grounds.) That is, higher TSP values are
75
-------
associated 'with" higher"activity levels, in accord with expectation.
The negative correlation between TSP and §3 is both unanticipated and,
even after the fact, somewhat puzzling. As stated above, such sign
reversals could arise because of multicollinearity. The next four
sentences may require reading. Suppose, for example, a dependent
variable y were directly but weakly proportional to a measured
independent variable x^ and directly and strongly related to an
unmeasured (or shadow variable) x^ Further suppose that x± and x2 are
negatively associated (i.e. as x-, increases, x 2 decreases). A
regression of y versus x -^would show a negative relationship, capturing
the dominant effect of the shadow variable x2« As long as the
relationship between x^and x2 remained in effect, the observed
correlation between y and x 1would be satisfactory for predictive
purposes - because indeed higher x -. values would be associated with
lower y values - but puzzling nonettieless (if xowere not identified as
a relevant variable). Now, recall from the ANOVA results that
significant differences among mines were shown to exist. Mine 1 in
particular had higher TSP values on average. If mine 1 had lower
activity levels for the variable 0.3 (on mine vehicles) this might
account for the observed negative association between QS and TSP.
Additionally, recall that the main effect for season 2 (trip 2) was
positive, though (unless mine 1 trip 3 is adjusted) not significantly
different from zero. These facts suggest a search for shadow variables
(should they exist) might start with consideration of these main
effects. Accordingly, dummy variables for mine and season were defined
and the correlation between these variables and the activity variables
computed. These correlation coefficients are displayed in Table 38.
Note from the circled values in Table 38 that indeed 0.3 is indeed
inversely related to both the dummy variable for mine 1, shown as DM1
on the printout, and the dummy variable for trip 2, shown as DT2 on the
printout. Note also the positive and strong correlations between each
of these dummy variables and TSP (a point which emerged from the
ANOVA). These facts suggest strongly that the observed negative
association between Q 3 and TSP shown in Table 37 is an artifact of the
data - reflecting that both mine 1 and trip 2 had lower Q 3 values on
average, but overall had higher TSP values. (The use of these dummy
variables in the regression is treated in the following section.) This
observation resolves the apparent sign reversal evident in Table 37.,
The second overall conclusion to be drawn from examination of the
simple correlation coefficients shown in Table 37 is that, by and
large, the correlation coefficients (r values) are not large. Wind
speed has the largest r value, 0.37, followed by Qi (the dragline
variable) with an r value of 0.33, and Qs (vehicles on public roads)
with a value of 0.25. All other variables have r values beneath 0.20.
The magnitude of these r values suggest that the resulting multiple
correlations will not be high or. in physical terms, that either
natural variability or unmeasured factors may be significant
determinants of TSP levels.
This conjecture is proven correct from an examination of the
multiple regression results shown on Table 39. Several stepwise
;': 76 :
-------
I
0'
77
c:
0:
-------
TABLE 39. A SUMMARY OF SELECTED REGRESSION RESULTS
.
Data set selected
Multiple correlations
coefficient , R
Order variables
introduced
Functional equation
constant term b iri
natural units
a
*
a
in
O
n
jj
0)
o
IH
a
fl
o a
o <»
33
> 8
u
Dragline, Ql
Coal haulage, Q2
On-Mine vehicles, Q3
Drilling, Q5
Blasting, Q6
Water trucks, Q7
Vehicles, public roads, Q8
Coal loading, Q9
Coal unloading, Q10
Scraping, Qll
Grading, Q12
Wind spaed
Precipitation
Predictive ability
Residuals plots
Signs of coefficients
Applicability to
entire data set
Consistancy with
literature
n
1
it
B
0
oi
•§
0.58
S1832
30.3
.13
.10
-.16
.10
.40
F
G
G
G
«-H
O
C
•H
0.96
S2189
31
.10
.46
-.18
.68
-1.02
E
G
E
G
CM
a
5
0.78
129
22,756
-1.06
-.41
.03
G
F
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regressions are" shown on this table, those including "all data points,
data points from each of the mines taken singly and finally data points
for each of the trips taken singly. For each regression, the table
gives the coefficient of multiple correlation, R, the coefficients of
each of the signficant effects variables,0^, and finally some
qualitative evaluations of the utility of the results. Table 40 shows
further details of the regression equation which includes all data
points. -• •-• ••• •••'• •• - •-• '
This equation including all data points is:
TSP - 30.3 Q°'13 Q°'10 Q^0'16 Q°'10 S0'40.
Since, based upon a priori considerations, it is necessary that each of
the exponents in the above equation must be greater than or equal to
zero, the exponent of QS is set equal to zero, and the resulting
relationship becomes,
TSP = 30.3 Q?'13 Q°'10 Q°'10 S0'40.
Some idea of the relative leverage of each of the variables in the
above equation can be gained by noting, for example, the effect of a
doubling of each of these variables. These effects, numerically equal
to 100 (20i) are shown below:
A Doubling of Would Increase TSP
This Variable Levels By This Percentage
Q-L (Dragline) 9.4%
Q2 (Coal Haulage) . 7.2%
Qo (Vehicles on
0 Public Roads) 7.2%
S (Wind Speed) 32.0%
Evaluated in this manner, windspeed has the greatest leverage.
The regression including all of the data points yielded an R value
of 0.5.8 (see Table 39) - significant in a statistical sense, but which
accounts for only (0.58) or 34 percent of the variability. Put another
way, random effects and exogenous factors together account for some 66%
of the variability in observed TSP values. This raises the question
"can this variability be explained by other factors?"
Some of the potentially relevant variables not considered in the
foregoing analysis include soil moisture (as measured at different
79 . _
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other activity variables^!a^gtly to be preferred to the
^
THE DISTRIBUTION OF PARTICLE SIZE
final point discussed
conducted on any of the four sxze ra g ^^ here> It may,
analysis strategy might well Para^eii^nificant correlations from the
is shown in the appendix.
81
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-------
BIBLIOGRAPHY
Allen, John R. Physical Process of Sedimentation; An Introduction.
George Allen and Unwin Ltd., London, 1970. pp. 92-117.
American Society for Testing and Materials. ASTM Standards on Methods
of Atmospheric Sampling and Analysis, Philadelphia, Pennsylvania,
1962.
Ash, D. On the Statistical Analysis of Air Pollution Data. Tech.
Report 19, Department of Statistics, Princeton University, New
Jersey, 1972.
Axetell, K. Survey of Fugitive Dust from Coal Mines. PEDCo -
Environmental, Inc., Cincinnati, Ohio, 1978.
Bagnold, R. A. The Physics of Blown Sand and Desert Dunes. Methuen
and Company, London, 1954.
Barrett, Larry B. and Thomas E. Waddell.. Cost of Air Pollution
Damage, A Status Report. National Environmental Research Center,
EPA, Research Triangle Park, North Carolina, 1973.
Bennett, C. A. and N. L. Franklin. Statistical Analysis in Chemistry
and the Chemical Industry. John Wiley and Sons, New York, 1963.
Beryland, M. E., Ed. Air Pollution and Atmospheric Diffusion. John
Wiley and Sons, New York, 1973. pp. 129-133. Translated from
Russian by H. Olacu.
Box, G. E. P. and Cox. An Analysis of Transformations. Journal of
the Royal Statistical Society, London, 1964. pp. 211 et seq.
Busse, Adrian and Zimmerman, John. User's Guide to the Climatological
Dispersion Model. E.P.A., Research Triangle Park, North
Carolina, 1973.
Cadle, Richard D. The Measurement of Airborne Particles. John Wiley
and Sons, New York, 1975.
Chamberlain, A. C. 1966. Transport of Lycopoduim Spores and Other
Small Particles to Rough Surfaces. Proc. R. Soc. A 296.
pp. 45-70.
83
-------
Cheremisnoff, P. N. and Angelo C. Morresi. "Predicting Transport
aind Dispersion of Air Pollutants from Stacks". Pollution
Engineering, pp. 26-30.
Chernoff, Herman. "Sequential Analysis and Optimal Design". Regional
Conference Series in Applied Mathematics, SIAM, Philadelphia,
Pennsylvania, 1972.
Clough, W. S. 1975. The Deposition of Particles on Moss and Grass
Surfaces. Atmospheric Environment, pp. 1113-1119.
Cowherd, C., Jr., K. Axetell, Jr., C. M. Guenther, and G. A. Jutze.
Development of Emissions Factors for Fugitive Dust Sources.
Midwest Research Institute, Kansas City, Missouri. Prepared for
Environmental Protection Agency, Research Triangle Park,
North Carolina, under Contract No. 68-02-0619. Publication
No. EPA-450/3-74-037. June, 1974.
Crawford, Jay and James K. Miller. Determination of Particulate
Emission Factors from Lignite Mining Operations. Division of
Env. Eng., North Dakota State Department of Health, Bismark,
North Dakota, 1976.
Daniel, C. Patterns in Residuals in the Two-Way Layout.
-Technometrics, Vol. 20 #4, 1978. pp. 385 et seq.
Dego, J., J. Toma, R. B. King. Development and Testing of a Portable
Wind Sensitive Directional Air Sampler. JAPCA, Vol. 27, 1977.
pp. 142-144. *
Dunbar, David R. Control Techniques for Particulate Emissions from
Unpaved Roads, Agricultural Activities and Construction.
!>resented at the 67th Annual Meeting of the Air Pollution Control
Association, Denver, Colorado, 1974.
Environmental Research and Technology. Air Pollutant Emissions in the
Northwest Colorado Coal Development Area. Environmental Research
and Technology, Westlake Village, California, 1975.
Faith, W. L. An Evaluation of Fugitive Dust Emissions. Presented at
the 67th Annual Meeting of the Air Pollution Control Association,
Denver, Colorado, 1974.
Fennelly, Paul F. "The Origin and Influence of Airborne
Particulates". American Scientist, Vol. 64, 1976. pp. 46-56.
General Metal Works. Operator's Manual, Model GMWL 2000 H. General
Metal Works, Inc., Cleveland, Ohio, 1977.
Harrington, R. E. "Fine Particulates — The Misunderstood Air
Pollutant". Journal Air Pollution Control Association, Vol. 24,,
1974.' pp. 927-929.
' ' 84
-------
l,^^ Dust
pp. 372-377.
HEW. Handbook of Air Pollution National Center for Air Pollution
Control, Durham, North Carolina.
- ' Environmental Protection Agency, 1974.
*
. I
; June, 1974.
^ I Katz, M. Measurement of Air Pollutants. World Health Organization ,
•• : Geneva, Switzerland, 1969.
.
Weatherfield, Connecticut, 1976
•i Research Triangle Park, North Carolma, 1971.
Lillis, J. and Dexter Young. "EPA Looks at Fugitive Emissions".
| JAPCA, Vol. 25, 1975. pp. 1015-1018.
fi- Lundgren, Dale A. and Harold J. Paulus. JAPCA, Vol. 25, 1975.
: pp.. 1227-1231.
i •-
D.C. , 1976.
r : ; 85
-------
America, April 21-22, 1971.
McNeal, W., et al., 1978 Keystone Coal Industry Manual. McGraw-Hill,
New York, 1978.
Monsanto Research
, North Carolina, 1975.
Addison
nafca, B. "The Role o| Wind in Pollution Dispersion".
JAPCA, Vol. 25, 1975. pp. 956-957.
^ ™ A ri 11 ottp 1977. Measurements of
rMass^ce^ion^o r Air^n Soil Particles.
Atmospheric Environment , 11.. PP- 193-l»b.
. Atmospheric Diffusion. 2nd Edition, John Wiley and Sons,
New York, 1974.
for U.S.E.P.A., Cincinnati, Ohio,
1976.
Roberts, John , and a
Association, Denver,
Colorado, 1974.
of
.Laboratory, 1975.
Scorer, Richard S. Air Pollution. Pergamon Press, New York, 1968.
': 86
-------
Smith, F. and D. Wagoner. Guidelines for Development of a Quality
Assurance Program. Vol. 4 - Determination of Particulate
Emissions for Stationary Sources. Triangle Research Park, North
Carolina, 1974.
Stern, Arthur C. Air Pollution. Volumes I, II, and III, Second
Edition, Academic Press, New York^ 1968.
Thornthwaite, C. W. Climates of North America According to a New
Classification. Geograph. Rev. 21:633-655, 1931.
Turner, D. Bruce. Workbook of Atmospheric Dispersion Estimates.
U.S. HEW, Public Health Service Pub. 999-Ap-20, 1970.
U.S. Department of Commerce. Technical Manual for Measurement of
Fugitive Emissions. Prepared for Industrial Environmental
Research Laboratory by the Research Corporation of New England,
1976.
U.S. Environmental Protection Agency. Surface Coal Mining in the
Northern Great Plains of the Western United States. Denver,
Colorado, 1976.
U.S. Environmental Protection Agency. Compilation of Air Pollutant
Emission Factors. Second Ed. Research Triangle Park, North
Carolina. Pub. No. AP-42, 1975.
U.S. Environmental Protection Agency. Development of Emission Factors
for Fugitive Dust Sources. U.S. Environmental Protection
Agency, Research Triangle Park, North Carolina. Pub. No.
EPA-450/3-74-037, 1974.
U.S. Environmental Protection Agency. Environmental Protection in
Surface Mining of Coal. U.S. Environmental Protection Agency,
Cincinnati, Ohio. Pub. No. EPA-670/2-74-093, 1974.
U.S. Environmental Protection Agency. Investigation of Fugitive Dust
•— Sources, Emissions, and Control. U.S. Environmental
Protection Agency, Research Triangle Park, North Carolina.
JE>ub. No. EPA-450/3-74-036, 1974.
U.S. Environmental Protection Agency. Supplement No. 5 for
Compilation of Air Pollutant Emission Factors. Second Edition.
U.S. Environmental Protection Agency, Research Triangle Park,
North Carolina, 1975.
*
U.S. Environmental Protection Agency. User's Guide for the
Climatological Dispersion Model. Research Triangle Park,
North Carolina, 1973.
87
-------
U.S. Department of the Interior. (Unpublished) Environmental Impact
Statement for Northwest Colorado Coal Development. Bureau of
Land Management, Denver, Colorado.
Wang I. T. and D. M. Rote. "A Finite Line Source Dispersion Model
for Mobile Source Air Pollution". JAPCA, Vol. 25, 1975.
pp. 730-733.
Woodruff, N. P. and F. W. Siddoway. "A Wind Erosion Equation". Soil
Science Society of American Proceedings, Vol. 29, 1965.
88
-------
APPENDIX A
MINE MAPS
The first figure in the appendix shows the legend used on the
maps. Following that are the maps themselves, one for each of three
visits to each of four mines.
Mines 2 and 3 represent different pit areas of the same mine. On
some visits, only one weather station was used for both areas.
89
-------
LEGEND
Dragline
Coal Loader
Coai Drill
Overburden Drill
Shove!
Sampler
Soil Moisture Collectors
Loading Facility (Tipple)
Weather Station
Blasting.
Reclamation Activity
Grading Activity
Topsoil Removal Activity
Coal Unloading Activity
Unvegefated Areas
Exposed Coal
Paved Public Road
Unpaved Public Road
Haul Road -In use
Haul Road-Not in use
Secondary Roads
Scraper Road
Figure A-l* Legend for mine maps.
90
-------
Potential Wind
Erosion Areas
! #4
Future Mining Area
MINE I
VISIT I
Figure A-2. Map for mine 1, visit 1.
91
-------
MINE I
VISIT 2
i4 METERS^
0 500
Figure A-3. Map for mine 1, visit 2.
92
-------
MINE I
VISIT 3
Figure A-4. Map for mine 1,'visit 3.
93
-------
POWER PLANT*
X-..J: :•.•:*.••;;••:•.•.**: :/r.". '• .r» r-f- 1^-*.^, -.-.
d •••-.. -«:?iim'
MINES
VISIT I
METERS
Figure A-5. Map for mine 2, visit 1.
94
-------
* POWSS PLANT
METEHS
MINE 2
VISIT 2
Figure A-6. Map for mine 2, visit 2.
95
-------
FOWEH P-ANT
MINE 2
VISIT 3
Figure A-7. Map for mine 2, visit 3.
96
-------
Topsoil Storage
Piles I
MINE 3
VISIT I
Wind Erosion Areas
Figure A-8. Map for mine 3, visit 1.
97
-------
MINE 3
VISITS
Figure A-9. Map for mine 3, visit 2.
98
-------
I
0
MINE 3
VISIT 3
Figure A-10. Map for mine 3, visit 3.
99
-------
Figure A-ll. Map
for mine 4, visit 1.
100
-------
Figure A-12. Map for mine 4, visit 2.
101
-------
Figure A-13. Map for mine 4, visit 3.
102
-------
APPENDIX B
COORDINATE GEOMETRY
The following distance and angle formulas were derived for use in
data analysis. They are based on the assumption that samplers and
sources are located at the same height. Point, line, and area sources
are considered.
POINT SOURCE
Consider a point dust source with coordinates D(XI, y^) and a
sampler with coordinates S(x2, 72)- The distance between them is given
by
d = >/*! - x2)2 + (y± - y2)2
To find the angle a between the wind and the line connecting
source and sampler, construct the triangles shown in Figure B-l. The
simplest solution is to apply the law of sines to the upper triangle,
but that produces a factor of tan 0 in the answer, which is
computationally inconvenient near 6 =90°, reflecting the fact that the
upper triangle does not exist for Q = 0°.
In the foregoing figure, SI is the wind direction (zero degrees is
North) and C is the midpoint of the line SD connecting the sampler and
the dust source. From the upper triangle in Figure B-l:
a = 180 - G - (180 - 0 )
=• 0. - G
Now we must find 0. Consider the lower triangle. We have,
g2 = e2 + f2 - 2ef cos 0
or
Where e — y
2
103
-------
ii
"*• C°0rdi^te geometry for _ .
* or a Point
so
104
-------
f -.
Substituting,
== Arccos
X1~X2
== Arccos
y2
= Arccos
y2
- 2yly2 + y2 - yl - 2yly2
== Arccos
2 + T (-4yiy2)
2y
~ Arccos
X- -X,
105
-------
Hence
a = Arccos
—
l 2
To find the distance "r" of the sampler off the centerline of the
plume, consider the diagram shown in Figure B-2. The distance is given
by
d sin a
LINE SOURCE
A line source can be treated as a point source by using the
midpoint of the line to calculate the following quantities: distance,
the angle between the dust path to the sampler and the wind, and
distance of the sampler from the plume centerline. The angle /3 between
a line source with endpoints (X-L, y-^) and (X2» y2> and the wind can be
calculated by considering Figure B-3.
0 = 180° - (90° - 8) - (180° - 6 )
• « Q + 0 '- 9QP
where
9 = A tan
X2~X1
The angle a between a line source and the line connecting its
centerpoint to the sampler can be found by use of the law of sines:
sin
Arcsin
X2+X3
Tf +TC
X2 X3
2
y2+y3
2
AREA SOURCE " _ :
Consider a rectangle square to the axes specified as:
1X2» y2)
106
-------
•wind
S = sampler
D = dust source
Figure B-2. Sampler distance from plume centerline.
107
-------
Figure B-3. Distance from line source to sampler.
108
-------
Its area is
- x)
For a rectangle with arbitrary orientation specified as:
w
, y,>
(x2, y2)
the area is
w V (xl "
- y2)
21
Let the sampler be located at (x3, y^. The distance from the
sampler to the center of the rectangle is given by:
d =
kl A2
- x.
- 7,
For a rectangle square to the axes described by the endpoints of a
diagonal, the length of the wind's path across the area to the sampler
can be computed as follows:
j
wind
109
-------
The wind path is: y = tan (90 - fi) (x - x3) + yg
The line containing a is: x = x^^
The line containing b is: y = y^
The line containing c is: x = x2
The line containing d is: y = y2
The wind path will intersect with at least two of these lines (more if
it passes through a corner of the rectangle).
Intersections will occur at the following coordinates:
(KI} tan(90 -
y~y
\tan(90 - fl) A3' *!/
(x9, tan(90 -Ji)(x9-xq) + yq)
^ £t O O
y2-y3
tan(90 - fl) + X3' y2,
These intersection points can be tabulated and compared with the
endpoints of the lines a, b, c, and d to see if the wind has passed
through the rectangle.
Case 1. There are two intersection points. The wind path
length is the distance between them.
Case 2. There are more than two intersection points. The
wind path length is the distance between the two
non-identical points.
Case 3. There are no intersection points. The path length
is zero.
Case 4. The wind is at 0°, 90°, 180°, or 270°. For two of
these cases, tan (90 -Q) cannot be calculated.
The wind path can be described by one coordinate
alone. It may miss the rectangle (length zero) or
pass through it (length a or b).
For the case where the rectangle is skewed with respect to the
axes and is specified by a center line and width, construct the
diagram shown in Figure B-4.
110
-------
\ e \
Figure B-4. Skewed area source geometry.
111
-------
No emissions disappear through any process such as
deposition, rainfall washout, or chemical reactions;
The weather conditions remain constant over time;
The wind speed .u is constant at all verticle heights
in which the emissions can be found.
Then, the total mass of emissions in the atmosphere between the
vertical planes x and x + dx perpendicular to the wind direction,
where x is a distance downwind from the source, is given by
: • j E
Crosswind integrated mass = — dx (C.9)
This very simple equation does not define the density of airborne
emissions at a point (x,y,z) per unit volume where y is a crosswind
distance and z is a vertical height. However, under the assumptions
above, this density must be of the form
! ,-»..,.-./ „ . .....
concentration = f(x,y,z) - . (C.10)
u
where _ ... JJ f (x,y,z)dydz =1.
One of the simplest types of dispersion models is the box model.
Assume that at a downwind distance x, all of the airborne emissions
are bounded between the crosswind distances -yx and y2 and the
vertical heights 0 and L. Assume further that the concentration is
constant within these bounds. Then the box model is defined by
concentration -
+
E
— (C.ll)
K
for all points -y±l y 1 72 and 0 _< z _< L. The values of y-^ y2, and
L may depend on x.
Many dispersion models use the box model for values of x which
are sufficiently large. The value of L is known as the "mixing
height" which is constant (to be discussed later). The assumption of
homogeneity in the crosswind direction would be violated if the wind
direction were constant. However, when several wind directions are
averaged together in a dispersion model, errors created by the
assumption of crosswind homogeneity have an average zero value. In
the climatological dispersion model, y± = 7 2 are crosswind distances
on a sector of 22.5 degrees width along the downwind direction.
-
While all dispersion functions are approximately of the form in
-.equation (C.ll), more sophisticated models make adjustments for
f;!
-------
turbulence. Even iii~ the simplest dispersion models other than the box
model, the function f depends on climatological variables such as
"wind stability class" (Pasquill 1974). A model which is often used
for moderate downwind distances x is given for fixed x by
f(x,y,z) - £L(y)f2(z) (C.12)
and f0(z) depends on a function of the form
The parameters
where -z is the.virtual image of z. (See Figure C-l.)
138
-------
•o
0
-p
o
«H
CD
0)
CO
0)
iH
O
•H
-P
^1
0$
ft
fl
•H
139
-------
Heavy particles fall to the ground at a terminal velocity v. At
the earth's surface, these particles are deposited. Then
f 2(z) - g2(z - H) (C.16)
where the centerpoint H includes the terminal velocity component in
the plume rise formula. (See Figure C-2.)
Assumption 2 does not adequately define deposition. If the
center line is proportional to x and the dispersion parameter crz is
proportional to x, then the fraction of the plume above the earth's
surface remains constant independent of x. In other words, the
crosswind integrated concentration above the surface is constant
independent of x. By definition, this implies no deposition is
occurring. A third means of describing deposition is given by the
formula
, concentration = f±(y)f2(z) £ (1 -D(x)) — . (C.17)
where f2(z) is given by equation (C.15 and D(x) is the fraction of E
deposited per unit time between the source and the crosswind plume.
Equation '(C.16) can be used together with either the Box model (C.ll)
or the reflected Gaussian model defined by (C.13), (C.14), and (C.15).
I
li
"J^ 140
-------
Vertical
distance
z . density g(z - H)
Figure C-2 . Tilted plume hypothesis,
141
-------
_ . APPENDIX..D ._._.., .
FIELD DATA
The following conventions apply to the field data tables:
TSP Levels TSP
-10 = missing value
Weather Data Wind Speed
__,. „ - . -_2 -=rwihd'directibn variable
-3 = wind calm
Precipitation
-2 = snow
Soil Moisture
-1 = missing value
Activity Levels
Activity levels were recorded in the following units:
Dragline (DRAG), scraper (SCRA), grading (GRAD),
drilling (ODRL, CDRL) in operating minutes per hour,
Blasting (BLAS) in holes,
Vehicles on public roads (PUBL) in vehicles per hour,
Loading (LOAD) and unloading (UNLD) in English tons,
Haulage (HAUL) and water trucks (WATR) in trips,
Vehicles on haul road (VEHI) in vehicle-miles per hour.
TSP size ranges are:
1 as 0-1.1 microns
2 = 1.1 - 2.0 microns
•142
-------
3 = 2.0 - 3.3 microns
4 = 3.3 - 7.0 microns
5 = 7.0 + microns
The four-letter activity codes
feasurement values are recorded " *e «™r°°hs wblch are not
ninesetq0£
cTluTsfor^ery^ne; visit, and hour.
Visits (trips) are numbered in chronological order for each
mine. ...
143
-------
&i
w "e
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rm^ft^^"9^'mir>n*i4m m *•* tn P» ••< « in «y in e» m « en m m ^ CN
Id
•O "Dj
tn m
tn
0) iJ H
i1 3
ffl «3>|
N cu!
in «
CU 0) fH
ca.
-------
g. I
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H
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a
o
ft Sj
I a
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n
a
K
mr4\Dr»*nfn(-tmeMintnin«Hp-t^r m o m in <
c o
-H +>
*J S
ti =
S«
tfiotninointnominoooinootnooirtOtnoininOinotrtotnoointnoootninirttn
I FH rj *n ff» O »H o\
_C»ie»J.OI..H ft CM tH
ft
145
-------
T>
£M
-
3.5-
m pi 13
.ceo
fc< •H
£&
3 g
-H C
m m
0} 4J
a s
W *E
9 V
ii
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iHCNntHN N« rtiHn »-i>»mmmin in rt rt f-i -H rtioui WTTr^r
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P-l
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146
-------
0!
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gs
inintnoiftOoooiflOoooooininmooinouioooomoouiominoomtfiuiin
133
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H a
CM m
•H i-i
•-* T in
147
-------
•D W
O I
n cu
CM »H «H t-H i
CM tH ^H »-*
C O
i-4 4J
JJ 9
>4 C
a-a
tnotnoooinooinointnintninoootnoooootnoootnoinotninomtnoinin
m m ^ H M
-------
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0} JJ
a. 3
a c
t^toreaio*Tif)*yfDC>Cif-'CQ*r*r\DtDvr*CD'*t~-\Dr*\DCDrf> to'to o a* tn CD \o
vr*r>ttD*F*T*T^'r*<3'' & ^r CD o « i
?.
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4i 9
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3*
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IfM HCM
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e-l
149
-------
ooomoooomomoomoomominininoinoijoooooinjnooooinooinin
fl) ft*
Sg
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-
150
-------
•C 0)
SO
-u
§* i
•-4 -H
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I a
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i-ifn»kO^OinOe>i(^Ot^'^'rico«voovD^if^Of-*poiftr^xj'*»»tnoin«eiinCTir«-p»o«a'p-\flr--m
•B g
5'
CO
151
-------
> 0
I 4J
< 3
t» I
5 J «,
•g| '
u I
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a js
y
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152
-------
•g g
SIS
II
?n
•H 3
, o
(N i-l (S
i1
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TJ «
S ,
I 6
•s
a g.i
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s
153
-------
-------
MIKE VISIT DAY HODB
\ 1 2U 16
17
18
19
20
21
•\ 22
.- 23
25 0
1 : " 1
j : 2
.3
4
5
• 6
7
8
- 9
; 10
~ E
11
12
13
'..••fB^MS'jgr »"-!• awr--- 1 *T
15
' , 16
-*•' •> '
\-- 17
. ^;- " T8
, r t-r • • -
<:"~-:' ' ' ' 19
~"'*v 20
• ^'L- • . -21
V' ' ' 22
. ! .'•, 23
. ~.f v. 26 0
•' - . ' "*" ' \1
— - »• .^»nr; niiMBi — - , O
r : " 3
L : ; • • 4
5
r ' ' ••- -v, • 6
[ , • ••' 7
8
] : -9
i 10
1 1
., - 12
.V • 13'
I i 1 ft
..,*,. ...... _15
lii
-17
rv
Speed
m.p.h.
1U.O
16.5
16.0
6.0
3»2
1.8
1.7
1.8
tt.8
6.3
5.5
4.6
3.6
4.0
2.8
3.2
9.9
-14..2
15.2
18.0
17-; 0
19.2
19.0
16.2
10.0
7.7
H -.9
2.5
3.0
7*0
6.0
11.0
11.0
12.0
10.1
.9-.-S
5.8
8.7
8.8
9.2
10.0
11.4
8.8
'11.0
11.0
8.5
8.5
-8*5
-7.5
5v5
154
Wind
Angle
180.
185.
195.
190.
-2.
^2.
-2.
-2.
180.
240.
30.
45.
60.
75.
-2;
-2.
135.
135.
135.
150.
:150i
240.
300.
285.
300.
-285.
"';-2v •
-2.
-2.
30.
-2.
285.
-300.
285.
270.
270.
265.
275.
270 v
255.
255.
-270,
270.
245.-
240 .
240.
240.
225.
-2.
180.
•reuiy. 'precip'. "staciiiry
OF (inches) Class
90.0
90,0
87.0
80.0
74.0
72.0
69.0
66.0
65 iO
67.0
64.0
61.0
60.0
59.0
60 . 0
67.0
70.0
,70,0
73 . 0
74
-------
0
VISIT DAY
1 26
lr
27
i;
HOUB
17
18
19
20
21
22
23
0
1
2
3
4
6
t-7
8
9
10
11
12
-13
14
wxna
Speed
m.p.h.
7.0
6.0
4.9
7.5
13.0
9.5
8.8
3.2
3.3
3.0
2.8
2.3
4.0
3.7
-2-«-9
4.8
5.0
5.2
3.0
4.3
-8 .vO-
9.5
Wind
Angle
240 .
240.
210.
-2.
210-
285.
300.
-2.
-2.
^-—2.
-2.
-2.
-2.
-2.
-—2.
-105.
180.
•--2." •
• -2. •
—2.
-60-*,
60.
Temp. precip. stability
F (inches) Class
73.0
73.0
72.0
56.0
60.0
55.0
53.0
51.0
50.0
-51.0
52.0
51.0
50 -.0
51.0
53*0
.55.0
55.0
55 -i 0
60.0
68.0
68^-0
66.0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
0
0
0
B
B
B
C
C
D
D
D
F
.F-
F
F
E
E
B
C
C
D
D
B
B
•B
15 8.5 330. 64.0
155
-------
MI2T2 VISIT DAI HCUB
1 2 21 8
; ; 9
; 10
11
12
• 13
14
15
16
17
18
19
20
21
22
23
22 0
1
2
3
•-4-
5
6
7 '
8
9
10
11
12
13
14
: is
16
r; ' 17
'i 18
1 -19
, 20
21
I,; . . ^ _
23
23 0
L ...-I...
2
[V - 3
U, -4
5
r e
ll -*
. 8
r 9
I 10-
Wind
Speed
m.p.h.
7.3
9.3
9.7
10.8
9.0
7.0
6.4
4.. 8
-5.2
5.1
3.8
*6-
1.1
1.0
1.8
3.4
2.1
3.2
2.7
1.3
3.0
2.9
2.3
2.8
2.8
2.5
6.5
12.2
13.6
11.2-
8,8
10.2
9.2
10.1
8.7
-8.9-
8.1
6.0
5.0
5.2
3.8
._7^_1
T.-7
-7 • *
-6-*-T
5.2
4.7
•2-9.
2.6
5.9
-7.4^
Wind
Angle
180.
165.
180.
170.
150.
150.
120.
125.
110.
120.
125.
.-2.
320.
-">— 2(T
--2.
300.
295.
295.
295.
300.
-280.-
290.
300.
330.
330.
4 0.
350.
0.
0*
-0,
0.
0.
345.-
345.
330.
-35S.
355.
350.
i -• :-"/» "-•
0 .
350.
0.
^.55*7
350.
T350v
•^==-='-0« -
350. .'
340.
_-2»-
;-2.
290.
"295V
Temp.
-18.0
-16.0
-14.0
-11.0
-9.0
-4.0
^0
—1^0
- -9-iO
-11.0
-12.0
-16.0
-16.0
-15VO
-14VD
-17.0
-19.0
-18.0
-20.0
-19.0
^20.0
-20.0
-21.0
,-20 . 0
-13.0
-10.0
---8.0
-7.0
-5.0
-'-5-.-0
-5.0
-5.0
-5.0
-5.0
-5.0
-—5-* 0
-5.0
^5 . 0
--5 VO
-6.0
-6.0
--7^0
-ff.O
. -8 ..0
— 8 iO
-8.0
• -8.0
-^•8».0-
-5..0
-3.0
*—~ v-Q
Precip.
(inciies)
-2
-2
•%
-2
-2
--2
-2
-2
-2
-2
-2
--2.
-2
:-2
—2
-2
-2
-2
-2
-2
•—2'
-2
-2
-2
-2
•r.2
•'— 2~
-2
-2
- ^2 .
-2
-2
—2
-2
-2
_—2-
-2
-2
-2
-2
-2
,-2
-2
-2
—2
-2
-2
.--2
-2
>-2-
--2'-1
Stability
Class
D
B
-B
B
B
-B
A
A
>g_ ' . :
£
F
.-•p..
F
F
-F -
F
F
F-
F
F.
F-
F
F
_B- '.
A
A
-B-
D
D
•C-,
C
c
-•C
D
D
-IX
'D
D
~D ^ -
D
D
-:D
D
D
-B
D
D
-D
"B
c
c-
156
-------
HIKE VISIT DiY
1 2 23
HOUR
11
12
13
14
15
16-
17
18
19
20
21
22
23
Wind
Speed
-in . p . h .
10.6
14.9
15.3
12.1
13.6
14 -f-7-
10.5
6.8
8.7
8.0
9.0
10.0
6.0
Wind
Angle
295.
300.
300.
295.
300.
--3 00.-
300.
280.
285.
275.
275.
J295.
300.-
Temp.
o
°p
-2.0
-3.0
--3.0
-5.0
-6.0
— 9.0
:: -9 . 0
-11.0
-12.0
-13.0
-11*. 0
— 14 . 0
-18.0
Precip.
(inches)
-2
-2
-2
-2
-2
*%
-2
-2
-2-
-2
-2
*%
^— & -
-2
Stability
Class
C
0
D
D
D
D
D
D
D
D
D
D-
T>
fi:
157
-------
i KIKE VISIT DAI HOUR
' i 1 3 190
1
; 2
3
4
••• : - 5
6
7
"h 8
„,; 9
10
11
12
; . 13
14
15
• . 16
r -17
18
19
, ' . 20
21
22
23
20 0
1
-2
3
4
5
* ; "6
i ' ! '• • 7
1 : - •- 8
9
I*: • 10
i 1-1
12
13
14
15
: 16
••; 17
18
rr - 19
h ' 20
L .21
H 22
23,
{ 21 0
! - 1
I;
Wind
Speed
m.p.h.
12,7
16.2
15.3
14.6
15.0
16.7
13*2
14.3
m-7
18.4
20.1
22. 2
23.3
24.2.
23 ; 9
21.6
21. 1
24.6
24.0
27.0
21 i 8
23.5
25.0
24,-0
24.4
24.6
25;^
19.8
18.4
-17..3
19.5
18.0
9.4
10.8
13.7
16. =3
18,:0
22.8
20.2
14.5
8.5
1-1*9-.
8 .4
'3^2
5.7
3.7
6.2
,3^-S-
10.7
:6;5
T0v5
Wind
Angle
265.
260.
255.
260.
270.
275.
265.
275.
-265.
260.
265.
270.
275.
275.
280.
280.
280.
280.
280.
290.
290 v
280.
280.
285-,
285.
285;
280;
285.
280.
285.
290.
275.
275.
285.
280.
-280.
285.
285.
-290J
275. :
270.
270-.
275.
>2 .
• ^2v .
-2.
215.
..._-2,-
210.
. -2.
-185r
Temp . „ . _ . . . , . .
Precip. Stability
P (inches) Class
10.0
10.0
10.0
9.0
8.0
6.0
5.0
5.0
5.0
6.0
7.0
7,0
6.0
5.0
4?0
3.0
2.0
1.0
.0
.0
-1^0
-2.0
-3.0
--r3+Q
-4^0
-5.0
-6 ; 0
-6.0
-6.0
-5.0
-5.0
-4.0
-2.0
.0
3.0
5,0
7.0
"8.:0
8 •-. 0
7.0
5.0
4»rO
2.0
1.0
1.0
1.0
.0
... -3*0
: .0
• .0
: ... _. v(J..
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
—2
-2
-2
-2
-2
-2
.^2
-2
-2
5-2
-2
-2
--2-
-2
r2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2-
-2
—•2
-2-
-2
-2
:-2;
:-2
~2
-2
-2
-2
-2
-2
-2
-2
D
D
D
D
D
D
D
D
D
D
D
D-
D
D
D
D
D
D-
D
D
D
D
D
-D
D
D
TD
D
D
D
D
D :
- c
c
D
.D
0
D
D
D
C
C
C ;
P
P-
F
P
-P-
E
P
E--
158
-------
MINE VISIT D&Y
1 3 21
22
HOtJB
3
4
5
6
7
-8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
0
Wind
Speed
ro. p.h.
10.0
9.4
10.7
10.3
10.6
-13.1-
14.3
11.9
17.0
8.4
8.5
. 8. 8
10.3
11.1
6.0
7.1
8.9
-5* 1
3.8
5.2
5.1
2.4
Wind
Angle
185*
190.
200.
195.
195.
1 90-.
195.
195.
195.
180.
-2.
-2.
190.
195.
-2.
-2.
-2,
:-2.
-2.
-2.
-2i-
-2. -
Temp
O
°F
1.0
2.0
2.0
3.0
3.0
5.0
8.0
14.0
15.0
18.0
21.0
19.0
-15.0
16.0
16.0
16.0
18.0
19.0
18 .0
16.0
17.0
16.0
* Precip .
(inches)
-2
-2
-2
- -2
-2
-2
-2
-2
— 2-
-2
-2
-2
-2
-2
-2
-2
-2
-2
~2
-2
-2
-2
Stability
Class
S
E
E
E
E
-.».
0
D
D
C
D
L
D
D
D
D
D
D
D
0
D
D
159
-------
1
': ]
[.:'
B.
ft
I'
J
f-'S .
i ;
^
f"[
r
('-'•
li
r
L:
, .
) !
I..:.
[ i
I::
( '•''•
1
f '•
I;-;
{?T
<*.
::
,:,
{'•<;:
'!-,'
:t
1
n
1":
(-T
MINE VISIT" '"DAY"' ~ HOUR
2 1 8 23
9 0
1
2
3
4
5
i 6
•- 7 -
8
9
10
11
12
13
14
15
. 16
17
18
20
21
22
23
10 0
•- • -\-
2
3
4
5
6
... 7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
11 0
1
' speed
m.p. h..
15.8
15.0
15.0
15.0
15.0
16. 9
17.0
18,0
19.8
23.8
21.0
20.0
16.0
14.7
18; 3
16.9
18. 8
18.4-
16.0
13.5
13.9
13.9
11.0
18.2
21.0
20.0
6vO
11.5
13.6
10,0
9.7
9.0
7.4
4.1
5.0
5.8-
7.4
8.0
10.8
10.5
13.0
15.-0-
13,2
13.6
11=3
11.0
10.0
11. 8
9.0
11.0
1 1 . 8
Wind
Angle
120.
120.
1 30 i
130.
135 i
130.
:130.-
135.
140.
150.
165.
160.
T65.
•165.
-1 65 .-
150.
165.
-170.-
160.
140.
140.
140.
90.
1 50^
165.;
160.
-^— 2; "
210.
240.
— ~2»
"••"2.
285;
255.
210.
285.
290.
315.
315;
330.-
10.
350.
150.^
0.
."15.1
"15.:- • "
30.
355.
270.
255.
50.
t);
160
=•- precip. bc'at»i'j.a.ry
°F (inches) Class
60.0
59.0
58 • 0
58.0
57.0
57-A.-0
52.0
.51.0
53 . 0
52.0
52.0
50 .-0
51.0
53 . 0
60.0
68.0
70.0
72 . 0
73.0
73.0
:73i-0
71.0
69.0
67,0
63.0
60.0
60 .0
60.0
58 '.0
-59.0-
58.0
58.0
58.0
71.0
70.0
.72 .-0
77.0
78.0
80.0
79.0
75.0
J78-*0
80.0
78.0
72.0-
72.0
70.0
61 . 0-
58.0
59.0
59 -.0-
0
0
0
0
. 0
-0,
0 ,
0
o
0
0
0
0
0
0
0
0
0
0
0
6
0
0
.0
0
0
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
-0
0
0
0
1
1
1
1
1
1
D
D
D
D
D
.... D
D
D
-D
D
D
- D
D
D
D
C
D
D
D
_D
-I>
D
D ;
-D
D
D
D~
D
D
D-
D
D
D
D
D
-D-
D
D
D
D
D
D
D
0
jj:
D
D
- D
D
D
D-
li
i,:
-------
MINE VISIT DAY HCUB
2 1 11 2
3
u
- 5
6
7
8
9
10
11
12
13
14
15
16
17
18
-19
20
21
22
23
Wind
Speed
m.p.h.
8.0
10.8
13.8
10.0
9.4
10.7
8.5
11 . 4
10.6
13.5
11.5
12*0
12.0
12.0
11.5
11.8
13.0
-1.2.0
12.2
12.7
10.0
7.5
Wind
Angle
285. .
320.
330.
330.
245.
30.
55*
30.
40.
45.
60.
75.
75.
75,
-60.
75.
60.
45.
60..
60.
€0.
60.
Temp.
o
F
58.0
57.0
55.0
53.0
53.0
53.0
55.0
53.0
51.0
51.0
55.0
:59,0
€0.0
62.0
63.0
65.0
65.0
62.0
59.0
55.0
50.0
48.0
Precip.
(inches)
1
1
1
1
0
-0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
-0
0
Stability
Class
D
D
D
D
D
D
D
D
D
D
D
!>
D
D
C
C
D
B-
X>
D
D
D
161
-------
; MINE 7isrr SAI BOIJJ
22 8 23
90
1
2
3
4
. -5
6
! '• • - 7
* 8
: . 9
?i 10
11
12
.13
i : 14
15
16.
17
18
19
20
21
-22
23
10 0
-r
1 : 2
. ' ' - 3
4
• : '. 5
6
' -7
8
9
10
11
12
• •• -13
14
15
: 16
17
V ' . ' 18
-, -.. 19
i 20
r 21
22-
L 23
r,; 11 0
:, si:
j'
.Wind
L Speed
m.p.h.
12.7
1U.4
U.6
13.4
9.5
10.2
10.1
8.9
9.3
10.6
12.1
18.0
17 . 1
17.3
16.5
20.4
20.9
18.9
11.5
9.5
9.1
8.5
7.9
8.-7-
6.4
6.5
6.0
2.1
2.9
3. Or
•3.v 3
.1.8
1.8
4.5
7.9
8 .-1
9.3
7.6
11 ; -I'-
ll. 0
11.9
-30*3=
9~T
10..7
9.0
8.5
8.5
-,7.-4-
6.6
7^0
8;5r
Wind
Angle
330.
315.
320.
320.
310.
300-
300.
300.
300V
300.
315.
340.
335.
335.
330 i
330.
330.
330-.,
330*
305.
300.-
290.
290.
290.
-2.
-2.
• --2^
-2.
-2.
---2.- -
'-2.
-2.
-2.
250 .
260.
270,
270.
280 .
-305.
300.
300.
-3-15.-
285..
290..
305v-
305.
300.
295,
285.
300.
-2.
162
Temp.
°p ,
20. 0
19. 0
18.0
17.0
15.0
-14.0
13.0
12.0
12.0
13.0
17.0
21.0
24.0
25 . 0
28.0
28.0
29.0
29 .,0
27.0
24.0
22vO
21.0
19.0
20-. 0-
19.0
15.0
14.0
13.0
11.0
-13^0
14.0
10.0
10.0
15.0
19. 0
25,0
27.0
30.0
32:iO
34.0
34.0
-32..0,
30 . 0 •
28 . 0
-25vO
22.0
22.0
21^-0-
19.0
19,0
-I9.0"
Preci
'inche
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-0.
0
0
0
0
0
,0-
0
0
0 ;
0
0
-0-
0
0
0
0
0
-0
0
0
0
0
0
.0
0
0
o
0
0
.0-
0
0
<)-
,...-• . * . . . ,
p. Stability
s) Class
D
D
B
D
E
E
E
E
D
B
C
-c
C
c
c
c
c
-C,
c
E
'E
E
E
.E.
F
P
-If?"
7
?
P-
Tf
T •
D
B
C
-B ;
B.
B
•B
B
C
-B-
D
D
' E;'-
E
E
....E
E
E
y- ,
-------
HINE VISIT
2 2 11
HOOE
2
*•
3
4
5
6
-7
8
9
10
• w
11
12
13
t •*
14
15
16
17
18
I W
19
20
21
A* *
22
23
Wind
Speed
..m.p.h.
7. 1
8.0
5.0
5.4
3.9
2.5
3*3
2.0
4.5
4.5
5.7
-7,0
7*8
9.8
9.3
9.9
10.1
10.0
10.5
12.4
12.4
12.6
Wind
Angle
335. '
340.
-2.
-2.
0.
0.
-2.
-2.
60.
-2.
100.
90.
115.
135.
135.
125.
130.
130.
145.
145.
145;
130.
Temp. precip. stability
°F (inches) Class
19.0
17.0
15.0
15.0
12.0
11.0
11.0
16.0
17.0
20.0
23.0
26 .0
28.0
29.0
27.0
23.0
19.0
16.0
15.0
13,0
12 . 0
12.0
0
0
0
0
0
0
0
0
0
0
0
-0
0
0
0
0
0
0
0
0
0
0
' £ •
£
F
F
F
• B
A.
&
B
B
B
-B
B
B
B
D
B
-E
'E
D
D
D
163
-------
: MINE VISIT DAI HOUR
2 3 110
1
2
J • 3
•} 4
J : 5
1 6
i • -
8
-•! 1 ''"' '~ 9
10
11
, 12
13
li 14
15
16
• 17-
t8
r 19
20
21
22
-23-
[ ; . 12 o
: ..j
• . - *V*—
' - 3
4
' *' -5
'••' 6
: 7
P 8
!:' • . 9
10
f" . -11
••: "12 .
13
I 15'
1 16
-17-
h • ; '•- 18
b 19
20
n 21
i- 22
-23
f ••' 13 0
hi ' • - - 1
! -2
f"
Wind •
Speed
ra.p.h.
4.0
3.0
4.0
.0
3.0
.0
4.0
5.0
6.0
4.0
.0
.0-
.0
.0
-.0
3.0
4.0
4.0
6.0
5.0
6.0
.0
.0
-5,0-
3.0
3.0
. 0
.0
3.0
4..0
.0
3.0
3.0
6.0
5.0
.3,0
6.0
5.0
3.0
4.0
6.0
-14»0
13,0
13. 0
-11. -0-
13.0
11.0
12.0
18.0
13.0
16.0
• —
Wind
Angle
' 50.
80.
60.
0. .
70.
0.
70.
70.
60.
60.
0.
0*
0. .
0.
0.
70.
60.
130.
120.
140.
120.
0.
0.
•SO-
SO.
120.
-o;
o. :
130.
100.
0.
140.
170.
170.
170.
140-»
230^
240.
240.
300.
340.
-.330*
310.
330 i
320.
320,
330.
320.-
330.
330..
340 v
164
Temp. Precip< stability
F (inches) Class
-19.0
-20.0
-19.0
-19.0
-19.0
-19.0
-19.0
-19.0
-19.0
-18.0
-14.0
— 11.0
-8.0
-3.0
.0
2.0
2.0
-.-1.0
-1.0
-1.0
-2.0
-4.0
-5.0
--6.0-
-3.0
-1.0
^3.0
4.0
4.0
-4,0
5.0
-1.0
7.0
8.0
9.0
-4.1^-0-
14.0
11.0
19 iO
20.0
21.0
-16-.-0,
14,0
13.0
12.0
11.0
11.0
.-9. 0,
8.0
7.0
8.0
-2
-2
-2
-2
-2
-2-
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2.
-2
-2.
-2
-2
-2
-2.
-2
-2
-2
-2
-2
r— 2
-2
-2
-2
-2
-2
--2-
-2
-2
-2
-2
-2
.•^2-
-2
-2
-2
-2
-2
--2
-2
-2
-2
F
F
F'
F
F
.- — F -
F
F
F-
B
B
-B,
B
A
A "
A
B
D
D
D
.jr
D
D
J)
D
D
D
D
D
D
D
D
-B
C
C
.B.
C
C
B
B
C
D
.D
D
D
D
D
- D
D
D
D-
-------
KIHE VISIT DAY
2 3 13
14
HOUB
3
4
5
6
7
Q
9
10
11
12
13
14
15
16
17
18
19
20
2 1;
22
23
0
1
Wind •
Speed
-m.p.h.
11. 0
12.0
11.0
: 9.0
10.0
12.0
10.0
6.0
8.0
6.0
6.0
«.o
9 . 0
4.0
4.0
4.0
.0
-4-.O-
3.0
.0
-i-o-
.0
.0
Wind
Angle
360.
350.
350.
350.
350.
330.
350.
330.
330.
340.
320.
300.
310,
300.
300.
320.
0.
180.
240.
•- 0 .
0.
0.
0.
Temp.
°F
6.0
5.0
5.0
4.0
5.0
5.0
4.0
5.0
6.0
6.0
7.0
7.0
7.0
9.0
7.0
5.0
2.0
-1.0:
r-UO
-2.0
--5.0-
-5.0
-1.0
Precip .
(inches)
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2.
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
Stability
Class
D
D
D
D
D
D
D
C
c
C
C
- c-
c
B
-c
D
D
&
D
.D
-D
D
D
165
-------
MINE VISIT DAI HCUE
13
14
12
13
14
15
16
17
18
19
20
21
22
23
0
1
2
3
4
5
6
-7;
-8
9
10
11
12
11
14
15
16
-17
18
19
20
21
22
23
Wind
Speed
m.p.h.
7.0
7.0
6.0
6. 8
5.2
11.0
8.8
9.2
7.6
5.2
4.8
3.-0
3,0
3.0
^- 8:
8.0
3.7
S.-O
8.0
12VX)
14. =-8
15.0
12.0
-16.0
26.8
20.0
— 8V8-
14.2
10.6
18 . 0-
1672
-1.0
-1.0
-1.0
-1.0
?.. 1-.-0-
Wind
Angle
315.
330.
345.
340.
315.
350.
10.
10,
15.
40.
45.
-2.
-2,
,-2,
90 i
80.
-2. '
-7S»-
105;
105.
105.
110.
105.
-120^-
135.
140.
-2.
120.
30.
:105,
120.
-1.
-1.
-1.1
-1.
-1_»
Temp.
°F
51.0
51.0
52.0
55.0
58.0
55 . fl
55.0
54.0
52.0
51.0
49.0
48.-0
46.0
46.0
47. -0"
47.0
46. 0
-45*0
47.0
50.0
;52.:0
53.0
55.0
.53^-0-
58.0
59 .. 0
•^62 .~0-
68.0
62.0
r74 . 0
72 .0
-1.0
-1.0
-1.0
-1.0
-t-,-0-
Precip.
(inches)
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
o
0
0
0
0
0
-0-
b
0
o-
0
0
-0
b
1
1
1
0
0.
Stability
Class
D
D
D
D
D
D
D
D
D
E
E
F
3
'F
E
D
E
E
C
C
D
D
D
-D.
D
D
C
D
C
D
D
D
D
D
D
D
166
-------
I.. , ' .....' - - •..- w — — -•.,.., .= .... -.. -
••• ; J3INS VISIT DAT HOUR
3 2 14 15
16
17
18
19
20
21
22
23
150
1
'* ; -2
... ' 3
4
5
6
7
8
9
10
11
: 12
13
1-4
15
16
17
18
19
20
21
22
23
160
'.'• 1
2
3
r " !
4
t • : 5
6
7
-B
9
['! 10
b; * ' 11
12
r 13
: 14-
1 15
16
i 17
Wind
Speed
m.p.h.
.0
4.6
.0
.0
.0
.0
.0
.0
.0
.0
.0
-0
»0
. 4.6
10.4
18.4
28.7
18. 4
26..S
20.7
20.0
24.2
20.7
-18^-4
20.7
24.1
17.2
9.2
6.9
.8 .-1
9.2 .
6. 9
6.9
6.9
5.8
-5.8
4,6
4.6
4.6
4.6
6.9
9,2
6'. 9
9.2
10.4
12.7
13. 1
2Q../7
18.4
16. 1
12.7
Wind
Angle
0.
330.
0.
0.
0.
0.
0.
0,
0.
0.
0.
0-.
0.
150.
270.
270.
270, :
,2-70.
270.,
270.
270i
270,
270.
2 70-,
300.
300.
300.
300.
300.
330.
330.
300.
, 270.-
270.
300.
240.
240*
2704
270.
240.
270.
300,.
270.
300^
300.
300.
300.
330.:
330,
330.
330;-
167
Temp.
°F
52.0
50.0
48.0
40.0
43.0
31-, 0
32.0
34.0
33.0
33.0
29.0
26. 0
30.0
31.0
32.0
42.0
42.0
35. 0
38.0
38.0
40.0
42.0
42.0
-42^13-
45 iO
45.0
42.0
39.0
35.0
-33 ,,0,
32.0
32.0
-30.0
30.0
29.0
-27 .0.
27.0
25 * 0
25.0
24i 0
27.0
-27^0
29^0
33^0
36*0
39.0
41.0
-42:^0
42 -.0
41 . 0
39.0
Precip.
(inches)
0
0
0
0
0
-0-
0
0
0
0
0
"0
0
0
0
1
1
0
0
0
0
0
0
.0-
0.
0
-o
0
0
0
0
0
0
0
0
-0
0
0
o
0
0
0
0
0
0
0
0
0-
0
0
0
Stability
Class
B
B
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
D
-D- ':
C
C
•D
E
E
-E- .
E
E
E •
E
F
-F.
F
F
F
F
B
B-
B
B
-B-
C
C
-C
c
D
D
-------
p .Wind ' Temp. precip. Stability
" BTSE VISIT' DAI HOUR Speed Wind@ Op (inches) Class
3 2 16 3:¥s; :IU •
s;:
f;'
i
r *
li
(f
n;
Si «:2 •'«•' 1- 5
» "8- I: : --5
-IA tt ^00. J i . u y
21 13-8 '»: 31:0 o »
270,
300,
T 11*5- 300. -o-n 0 E
11 1I-8, 1™:- i-S- s E
n 2 iKs. «: S:oD -°
168
S i i I
fi 16.1 300. 30.0 0 "
1 34.5 300. 30.0 0 C
-I 15: |: I
10 20.7 330. 32.0 0 £
11 \l:l liS: 1- | »•
S r,:6o US: .-S:5- » •
-------
ttXHE VXSXT BAT HOnB
3 3 17 14
15
16
17
'i ' 18
* 19
20
21
22
23
18 0
* 1
2
3
4
5
6
7
8
.,9
10
11
12
13
14
15
16
17
I 19
20
21
22
23 ,
19 0
2
• : . . 3
4
5
! 6
-7 •
... ' • 8
9
'?: , 10
? 11
w, * , '
12
~ : .13.-
14
-' i 15
16
Wind
Speed
m.p.h.
9.2
10.3
7.9
7.6
3.2
-5*5
4.1
4.4
10.5
13.6
4.9
14.5
15.8
14.7
13.9
17.0
14.4
-14.1
16.8
13.3
15.0
16.0
14.8
T4-. 7-
13*2
10.3
11 . 1
9.4
8.0
6.1
4.7
4.3
4.0
3.4
2.2
2.1
1 . 1
1.4
1.0
1.1
-1 • 2-
2.2
2.7
3.4
5.0
7.8
1-0.6-
10.6
10.1
8; 4
Wind
Angle
75.
115.
135.
105.
125.
195.
225.
225.
275.
265.
295,
285i-
285.
285.
275.
265.
265.
275.
275.
285;
285.-
285.
285.
265*
265;
275.
285.
285.
280.
280.
260.
-2.
-2.
-2.
-2.
-2.
-2.
-2.
-2.
-2.
-2*
-2^
-2.
90.
105.
120.
125..
120.
100.
100.
Temp.
°P
-16.0
-14.0
-13.0
-12.0
-12.0
-11 .0
-10.0
-11.0
-11.0
-11.0
-11 .0
—12*0
-16.0
-17.0
-20.0
-22.0
-23.0
-24 . 0
-25.0
-25.0
-25 iO
-25.0
-24.0
-22.T.-.0
-22.0
-20.0
-22.0
-22.0
-24.0
-24 . 0
-25.0
-25.0
-25.0
-25.0
r25 .-0
-25.0
-25.0
-25.0
-25.0
-25.0
— 25-*-0-
-25. Q
-25.0
-24.0
-21.0
-17.0
—15,0
-13.0
-13.0
-14.0
Precip. Stability
(inches) Class
-2
-2
-2
-2
-2
-—2-
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
--2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
"2r
-2
-2
-2
C
C
C
C
D
-D
0
D
D
D
D
-D
D
D
D
D
D
D
D
D
D
D
C
C
C
C
c-
D •
E
F
F
F
F
F
F
F
F
F
F
F
-F-
B
B
B
C
B
C
C
C
E
169
-------
BINE VISIT DAT
33 19
20
HOUB
17
18
20
21
22
23
0
1
2
3
4
5
6
7
8
9
10
11
12
13
1»
15
16
.Wind
Speed
ro.p.h.
a. 5
8.5
10.^
8.5
7.0
6.1
6.6
8.5
7.9
7.7
12.6
9. 1
7.7
4.5
•8i i
6.3
9.4
12^-6
12.2
9.2
:9.5
10. 4
12.1
14.0
Wind
Angle
120.
120.
125.
105.
115.
-2.
100.
100. .
100.
100.
120.
100.
110.
-2*
120.
135.
120.
120.
135.
.145.
150.
125.
135.
450.
Temp.
f*
°F
-17.0
-20,0
-18.0
-21.0
-22.0
-22.0
-23.0
-23.0
-24.0
-24.0
-21.0
-23 . 0
-24.0
-24.0
-23.0
-24.0
-21.0
-19.0
-16.0
-12.0
-10.0
-9.0
-8. 0
^8.0
Precip.
(inches)
-2
-2
-2
-2
-2
-2
-2
-2
_2
-2
-2
^«%
£t"
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2..
Stability
Class
P
E
£
E
E
F
F
E
E
E
D
E
E '
F
E
C
C
D
D
C
C
C
D
D
170
-------
I
SINE VISIT DJ.Y HOUR
4 1 21 16
17
18
19
1 20
i- 21
22
23
22 0
1
2
: ! '3
4
5
6
7
6
-9 -
10
^ 11-:
12
13
1 4
IS
16
17
18
1 9
20
21
22
23
23 0
1
r: 2
3
4
r 5
6
L 7
8
i - *
I.-' 10
11
f " . 12
U • 13
14
r\ is
16
{ 17
, 18
[:
Wind
Speed
m.p.h.
7.6
8.0
11.8
12.2
16.0
19-.-0
18.0
3.6
2.7
2.8
1.2
3.1
3.2
3.8
4.0
1.9
3.0
4.S
7.5
10.5
11.3
13.0
15.0
15.6
12; 7
11.8
9.2
7.6
6.0
8*2
10.1
11.0
11.6
11.8
13.5
12.5
11.0
3. -7
3.2
6*7
4.5
*,6
7.4
10.0
9.0
8.6
8.3
20.3
19.5
15*0
16.8
Wind
Angle
-2.
0.
350.
10.
20.
300.
330.
-2.
-2.
-2.
-2.
^2.
-2.
-2.
-2. .
-2.
180.
210.,
240.
270.
255;
270.
270.
2-70.
270 i.
315.
315.^
330.
360.
£0~
60.
60.
60.-
60.
60.
60--
60,
-2i
-2v
45.
-2.
60.
45.
105.
105.
135.
120.
120,
120.
90.
60.
171
Temp.
f^
°F
70.0
70.0
69.0
65.0
61.0
49.0
49.0
U9.0
48.0
47.0
46.0
46.0
49.0
49.0
49.0
52.0
55.0
57 . 0
60.0
63 »0
-70i-0
70.0
71 .0
72-0
73.0
72.0
71vO
70.0
65.0
62, .0.
58.0
55.0
52.0
52.0
51.0
50.^5
50 »0
51 . 0
55 ~. 0
53.0
62.0
65.^0
68.0
.72*0
77.0
80.0
82.0
71 ^0
72.0
68.0
65.0
Precip.
(inches)
0
0
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Q
0
0
0
0
0
0
0
0
0
0
0
0
0
o
0
0
0
0
0
0
0
0
0
0
0
0
0
,
Stability !
Class
8
B
B
C
D
D
D
D
D
E '
£
-S
2
E
B
B
B
C
C
C
D
D
D
U.
C
C
C
B
C
E-
E
E
D
D
D
-D
B
E
-1-
A
A
-&
A
A
B -
B
C
D
D
D
D
-------
SINE VISIT BAY
41 23
24
HOOB
19
20
21
22
23
0
1
2
3
4
5
6
7
8
9
10
11
12
13
1.*
15
Wind •
Speed .
m.p.h.
15.0
16.0
11.5
14.3
14.5
15.0
14.0
16.0
14.8
4.5
11.8
2.8
2.7
3.5
3.8
4*5
5.0
6.2
7.5
6,5
6.2
Wind
Angle
80.
70. .
75.
60.
75.
90.
90.
90.
90.
90.
90.
90.
-2.
225.
245.
-2.
-2.
»2^
-2.
~2.
-2;
Temp.
/^
°P
64 ..0
58.0
52.0
50.0
50.0
50.0
50.0
49.0
48.0
48.0
48.0
51.0
61.0
65.0
72.0
73.0
78.0
80.0-
78.0
81.0
78.0
Precip.
(inches)
0
0
0
0
0
0
0
0
0
0
0
0
,0
0
0
0
0
0
0
0
0
Stability
Class
D
D
D
D
D
D
D
D
D
D
0
A
A
A
A
A
A
A
B
B
B
172
-------
Wind
HZT5E VXSIT D*-* HOUR Speed
; . . lU.p.n.
a 2 1 o
1
2
3
i a
, : -.5-
6
7
; 8
"' , 9
10
• ; 11
.12
13
1ft
15
16
17-
18
19
20
21
22
23
2 0
1
2
3
4
•'• : • 5 -
6
7
8
9
10
11
12
13
14
15
16
17
18
19
r-:; . 20
'I' 21
.22
,-.\ 23
!•'• 3 o
L 1
2
r> " '
5.6
6.7
8.8
6.2
4.0
4»1
4.6
6.0
7.7
7.8
8.6
12; 9
, 16.7
16.5
12; 5
8.1
5.9
-5*-2.
4.8
6.3
10 . 6
13.4
12.2
14 .-4
16 . 4
16. 5
15.0
13.5
11.5
13.*-3
14.2
11.8
11.0
7.1
8.1
8.4
6.0
4*5
5. 9
2.4
1.4
12.0
15.4
17.8
"20.0
18.5
17.9
-16.0
13.6
14 . 1
16.9
' Wind
Angle
-2.
285.
300.
300.
-2.
- «2»
-2.
-2.
255.
250.
250.
260 i
240."
240.
230;
235.
270.
270v
270.
235.
215.
215.
225.
-225.
230;
230.
230.
230.
230.
240-p
235 i
240.
250.
265.
-2.
345.
-2.
-2.
70i
-2.
-2.
215.,
210..
210.
220 .
220.
215. .
2-10.
-2.
230.
230".
173
Temp.
°F
12.0
9.0
11.0
10.0
7.0
- 7^0
10.0
10.0
11.0
12.0
16.0
18.0
I8.0
17.0
16.0
15.0
11.0
11 .0
11.0
12.0
14.0
16.0
18.0
19.-Q-
20.0
21.0
22.0
22.0
22.0
23.0
25.0
23.0
26.0
26.0
28.0
24-^A
24^0
23.0
22. 0
21.0
20.0
29 .rQ
30 10
30 . 0
30 wO
31.0
30.0
29.0-
29.0
30 ..0
32 . 0
Precip. Stability
(inches) Class
^2
-2
-2
-2
4-2
~^2".
-2
-2
-2
-2
-2
-2:
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
*»*?
-2
-2
-2
«•»*?
~2
-2
-2
-2
-2
-2,
-2 :
-2
-2
-2
-2
-2
-2
-2
-2
P
£
E
F
F
- -.F
F
F,.
C
c
C
D
T>
0
D
C
C
•C
D
D
D
D
D
4)
D
D
D
D
D
^
O
D
• B
B
B
B
B
C
C
B
B
.D
D
D
D
D
D
D
D
B
. U
-------
VISIT DJLI HOOE S52d. Wind *£*• .Pr*cip. Stability
30
(J
m.p.h. Angle °P (inches) Class
3
4
5
6
7
-8
9
10
11
12
13
15
17
18
19
20
21
22
23
13,3
14.2
13.8
14.7
8.9
-8.2-
12.5
14.2
19.^4
18.6
19.5
14.7
15.8
5.3
5.5
1.5
5.0
5.3-
5.6
7.0
215.
215. :
270.
245.
-2.
270.
250.
240.
235 i
230.
220.
210.
215.
275.
285.-
-2.
305.
-305.
315.
300.
29. 0
30.0
26.0
30.0
26.0
31.0
33.0
36.0
40.0
41.0
42.0
40.0
40.0
13.0
10iO
7.0
10.0
-10.0-
10.0
: 15.0
-2
-2
-2
-2
-2
-2
-2
-2
—2-
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
D
D
D
D
C
C
D
D
C
D
D
D
D
B
F
F
F
.V
F
F
174
-------
,
: ttlUE VTSIT DAY HOSE
4310
1
2
: . .3
• 4
i : 5-
6
> ! 7
8
- ! 9
10
? 11
12
13
14
15
16
17
; 18
19
20
21
22
^ o
-ZJ
20
1
2
3
'•• ! 4
; : • 3
6
7
8
9
10
: 11-
12
13
14
15
30 15
16
; 17
18
1 , 19
r 20
21
22
? 23
3-10
1
"•
Wind
Speed
m.p.h.
9,0
14.0
14.0
9.3
7.2
-7.4
5.0
11.6
9.6
5.2
6.7
13.4
10.6
8.0
5. 1
2.4
2.0
4.7
3.2
2.1
3.7
3.0
2.2
1 n n
. -1 U ..\J-
11.4
10.5
16.2
18.2
17.0
18.0-
10.9
14.3
22.9
24.8
22. 1
24 .0
20.5
17.5
19;7
17.9
10.8
3*:?
1 » 2.'
3.3
3i 9
2.6
3.0
-4.6
2»1
2.0
2.4
Wind
Angle
30.
315.
310.
300.
255. .
240.. -
-2.
270.
270.
270.
285.
260.
240.
210.
-2.
-2.
-2.
285.
-2.
-2.
245i
240.
-2.
jyiin
•aS*tV .
240.
235.
210.
210.
210.
210.
210.
220.
225.
225.
225.
=225^
530,
225.
240;
230.
270.
-2-4
-2.
io.
15.
-2.
-2.
30.
30.
-2.
30v
Temp.
°F
-7.0
-10.0
-11.0
-12.0
-11.0
^10.0
-12.0
-5.0
4.0
6.0
12.0
15.0
17.0
17.0
16.0
16.0
13.0
10.0
10.0
6.0
6.0
7.0
8.0
*2 ft
W~fr ~W \f
15.0
15.0
18.0
19.0
19.0
20.0
19.0
19.0
21.0
21.0
23.0
-24 .-0
25 VO
26.0
26iO
25.0
15.0
11 » 0
5*0
3.0
3iO
1.0
.0
--1-»0
2.0
2.0
2.0
Precip.
Cinches)
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
—2
-2
-2
-2
-2
-2
-2
-2
-2
— 2-
-2
-2
^2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
^2^
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-«2
-2
-2
-2
...-•• A
Stability
Class
D
D
D
D
D
D
D
D
C
C
C
D
D
B
B
B
B
£
F
F
F
F
F
-0
D
0
D
D
D
-D
D
D
D
D
D
0-
0
D
D: .
D
C
E
F.
F
F-
F
F
-P
B-
D
D
175
-------
SINE VISIT
43 31
HOUB
2
«•
3
«^
^
5
•^
6
7
8
9
10
11
12
13
14
15
16
l **
17
18
19
1 ^
20
21
22
23
Wind .
Speed
- m.p.h.
2.0
2.5
1.3
1.9
11.8
17.5
20.5
2.6
21.*
23.2
18.0
12.0
5.9
5.3
3.3
6.1
6.0
8.2
10.8
7.6
6.5
7.0
Wind
Angle
-2.
-2.
-2.
-2.
240.
240.
225.
85.
300.
320.
310.
305.
270.
315.
300.-
15.
30.
30.
45,
45.
60.
60.
Temp.
°F
7.0
10.0
10.0
8.0
11.0
12.0
13.0
.0
5.0
4.0
5.0
6.0
10.0
•4.0
2.0
-2.0
-3.0
-5.0
-7.0
-7.0
-8.0
-8.0
Precip .
(inches)
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
-2
«•»*?
Stability
Class
D
D
D
D
D
D
D
D
D
D
D
C
B
C
D
D
D
D
D
B
D
D
176
-------
SOIL MOISTURE
KIKE VISIT SHIFT DA1^
1 2 1 22
23
24
2 2t
22
23
3 21
22
23
DATA
HCtJS MIS
0
0
1
0
23
9
11
12
13
7
8
10
7
8
16
17
16
16
17
0
10
50
55
10
20
55
0
0
30
25
35
50
us
45
0
55
10
45
15
55
5
10
45
25
35
15
25
0
10
35
45
20
30
0
10
%Mois-
ture by
Weight
4.0
2.3
3,5
3.2
3.6
3.3
4.8
3.7
3.5
4.4
5.4
5.5
4.3
6,3
7.8
5.3
3.2
2.4
3.9
2.7
3.6
7.5
9.8
4.2
4*0
5.3
5.1
3.6
.2.9
5.6
3.7
3.5
5.8
9,9
3.8
3,8
Cup
Number
1
32
10
22
23
29
2
1
19
8
16
6
11
29
%
25
14
6
23
24
10
27
24
20
2
26
16
20
27
28
40
19
28
14
4
26
177
-------
%Mois-
LISTING OF DATA BASS FOB FI1S SOIL ture by _ C^P
•S VXSIT SHIFT DA HOtJE HZH Weight
1 1 10
13
15
22
23
-24
2 11
13
14
15
22
178
13
14
7
8
14
7
8
9
7
9
11
7
8
14
7
8
14
16
.17
15
22
15
16
21
22
15
16-
15
30
40
45
25
30
35
40
10
50
30
55
0
10
50
0
40
10
35
20
50
20
25
20
50
SO
33
39
45
30
15
55
0
10
20
25
50
40
30
40
45
20
55
35
50
55
0
45
50
55
45
0
9.5
12.2
1.0
11.7
1.0.5
7.1
10.3
• 7.6
17.7
20.5
19.0
17.8
20.7
19.3
11.9
9.6
9.0
9.4
4.6
9.2
6.7
3.8
1.7
3.5
4.7
2.7
4.0
5.3
1.2
6.0
3.6
4.9
8.1
11.8
12.2
5.3
1 4 . 8
19-9
18.3
17.1
12.6
20.6
17.4
15.9
18.2
13.7
6.7
7.7
6.9
10.6
5.7
5
6
7
1
8
5
9
10
11
21
22
23
24
25
23
15
26
18
20
27
1
21
6
9
17
18
8
14
10
8
9
10
2
3
4
5
3
12
13
14
15
16
17
18
19
26
27
28
29
30
12
-------
%Mois-
LISTISG 0? DATA BASE FOB FILE SOILture by Cup
HIKE VISIT SHIFT D2L HOU2 MIN Weight Number
22
23
24
13
14
22
23
21
15
16
15
16
22
15
16
23
0
6
23
22
0
6
23
0
6
30
10
30
45
25
50
0
16
45'
0
35
0
10
35
40
45
15
35
35
25
40
30
45
50
45
25
55
10
.55
2.2
5.7
3.1
3.9
8.6
1.9
3.3
2.8
3.4
2.1
7.1
1,4
2.6
8.0
9.3
11.4
8.6
12.8
18.2
.5.4
1.5
2.5
5.5
3.4
5.5
3.9
2.4
3.2
3.7 ...
17
10
25
24
3
16
31
33
35
13
23
12
15
2
4
1
7
6
20
19
22
7
2
5
11
24
30
32
26
179
-------
%Mois-
-ISTIKC? 0? BA'TA BASS FOE FILE SOIL ture by Cup
MISE VISIT SHIFT DA HOTIP. BIN Weight
2 2 1 - 9 1
2
23
10 0
1
23
11 0
23
12 0
23
2 97
8
9
10 9
10
11 7
8
9 .
3. 9 15
17
10 1ft
16,
IT 14
15
30
40
23
55
55
0
5
40
45
50
55
35
0
5
10
40
55
50
35
30
45
55
0
5
10
35
50
55
0
5
0
15
10
45
45
50
55
.40
35
40
45
5,1
6,6
3.6
5.0
10.9
5.0
5.0
3.8
6.4
-1.0
3.7
5.9
1.5
3.9
3,1
5.0
6.8
5.4
.7.1
3.9
7.9
3.7
8.8
25.2
2.6
4.4
13.1
7.6
14.3
-1.0
3.0
4.9
16.3
8.9
-1.0
4.0
2.8
4.0
4.8
6.9
6.0
3.1
27
50
4
16
5
1
40
9
15
35
14
17
8
10
9
28
7
29
26
7
22
32
23
3
30
' .2
12
24
19
11
6
34
18
10
13
21
20
20
32
5
40
34
180
0
n
-------
%Mois-
LISTING OF DATA BASS FOE FILE SOILture by Cup
JUS'S VISIT SHIFT . B.\ HOUE «ZN weight
3 2 1
1
15
16
17
15
16
17
14
15
16
0
0
1
23
0
23
0
23.
6
7
7
8
7
15
16
15
16
15
15
50
20
5
10
15
25
10
15
20
0
40
45
0
5
35
UO
45
25
40
45
48
55
0
10
30
10
15
40
45
50
0
5
10
13
15
20
20
45
15
20
:30
.35
10
40
5
10
.15
20
5
11.9
14.5
4.4
3.1
3.4
4.5
4.4
3.8
3.1
4.6
19.8
3.3
4.3
6.4
4.0
8.3
3.7
4.0
5.1
4.3
6.7
9.4
7,5
6.6
11.2
6.4
4.1
5.1
6.7
3.8
-1.0
6.2
4.2
3.6
5.7
5.1
::.7
3.8
4.5
4.1
2.2
3.1-
4.9
9,8
4.0
4.6
5.6
5.9
4,2
5.0
3.5
2
28
27
40
8
4
17
.7
4
17
23
22
7
26
26
22
16
1
32
4
9
6
34
28
1
14
32
10
6
5
11
:. 1.
11
10
23
24
29
17
12
16
26
29
23
24
21
32
22
19
15
5
14
181
\-
-------
%Mois-
L1STING OF DATA B1S3 FOP. FILE SOIL ture by Cup
HIKE VISIT SHIFT .DA'. H00£ MXN weight Number
3 2 3 16 16 0 4.8 24
5 8,7 29
35 2.3 34
40 4.3 16
45 7.3 8
17 -1 -1 4.4 14
5.3 2
4.5 20
4.1 6
3.8 25
5.6 . 8
30 18 40 12.4 12
50 5.6 50
55 4.5 9
19 0 2.4 21
182
-------
%Mois—
.ISTISG 0? DATA BASL FOB FILE SOIL ture by Cup
HIKE VISIT SHIFT DA HOUS H1H Weight Number
22
23
*
24
22
23
24
22
23
24
7
9
11
6
7
8
6
7
8
15
16
14
15
16
14 .
15
16
23
0
22
23
0
0
40
10
35
45
20
50
20
25
55
30
35
40
45
0
30
10
30
45
20
50
25
50
0
30
0
35
0
10
25
40
30
45
50
15
25
45
55
10
9.8
9.6
9.0
8.4
5.5
4.6
9.2
6.7
3.8
3.7
4.7
2.7
4.0
5.3
5.7
2.2
5.6
5.6
3,9
1.7
3.5
8.6
1.9
3.3
1.2
2.1
7.1
1.4
2.6
5.4
1.5
2.5
5.5
3.4
2.8
8.7
3.4
2.4
3.2
23
15
26
18
11
20
27
1
21
26
17.
18
8
14
12
17
10
25
24
6
9
3
16
31
10
13
23
12
15
19
22
7
2
5
33
34
35
30
32
183
-------
%Mois-
LISTING 0? D&TA BASE FOE FILE SOIL ture by
MINE VISIT SHIFT DA HODS HI* Weight
21 11
2 0
1
3 0
23
2 27
8
3 5
3 1 15
17
2 15
16
3 15
16
10
15
5
15
20
0
30
45
50
55
35
40
45
10
40
43
45
50
45
50
53
10
40
45
50
15
10
15
15
45
22.9
1.4
5.4
16.6
9.5
1.9
1.0
8.4
5.4
8.5
9.7
9.7
7.2
1.0
1.2
5.9
2.0
9.1
5.1 '
12.8
1 1.4
3.2
16.7
6,9
9.1
1.8
8.6 .
16.8
.6
3.6
6
22
10
3
35
24
8
40
5
11
26
4
34
27
20
23
14
91
15
17
30
7
16
29
32
19
15
16
12
19
184
-------
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ACTIVITY C*TA BASE - PART 3
HJHE VISIT SHIFT DAT HOOH SEG7 QTJSHT7 SrG8 QtUNTS SKRa QTJRHT9
i 1 11 2
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-------
HIDE
TI C»T» B»SE - EAHT 3
VISIT SHIFT DAT HO0B
1 5
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7
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-9
10
11
12
13
15.
16
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19
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2.1-
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SEG7
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ACTIVITY E»TS BSSE - EAR? 3
BINE VISIT SHIFT C»I H008 SEG7 QOftNT7 SES8 CHUVMT3 S^Q p'l».?'T9
231 11 7
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9
10
11
12
13
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232
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SES7 QOAHT7 SEG8 QUAHT8 SEG9 OtU8T9
0 .0
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233
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aCTITITT tJT» BUSH - EAST 3
HItfB VISIT SHIM 0»T HOBS SEG7 QURHT7 S2G8 QOSHT8 SFG9 CWANT9
s * 33= it
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10
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ACTITITT CATA HAS! - FABT 3
HIKE VISIT SHIFT DIT HOOH SEG7 QUAHT7 SEG8 QOAHT8 SEG9 QDA»T9
W i t V;k~ TK 'T~ W o" " ~"".o. o .c
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235
-------
ACTIVITY E»T» BaSE - PART 3
HIHE VISIT SHIFT DM HOUR SE67 QWAHT7 SES8 QORST8 SEG9 OOAKT9
3 3 -3 12 «
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[1
RXlig VISIT SHIFT DAT BOHR
a 11 17
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11
12
13
14
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237
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ACT17ITT EST» BRSE - FART 3
HIKE VISIT SHIFT OUT HOUR S167 Q0HBT7 SZG8 QnSHT3 SEG" OWA3T9
4 1 ' 1 22 8
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238
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ACIITITI EAT* BiSB - FABT 3
6
SIIIB VISIT SHIFT D»T HOTIH
T
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HI»E VISIT SHIFT D« BOOH 3ES7 QOaHT7 SEG8 QtUHTS
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