United States Office of Air Quality EPA-450/4-85-001
Environmental Protection Planning and Standards January 1985
Agency Research Triangle Park NC 27711
Air
vvEPA Dispersion of
Airborne Particulates
In Surface Coal
Mines
Data Analysis
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EPA-450/4-85-001
DISPERSION OF AIRBORNE
PARTICULATES IN
SURFACE COAL MINES
Data Analysis
By
TRC ENVIRONMENTAL CONSULTANTS, INC.
Englewood, Colorado 80112
Contract No. 68-02-3514
EPA Project Officers:
J.L. Dicke
J.S. Touma
-al 1 Detection Ago
^r (5PL-16)
..'.-.not, Room 1670
Chicago,
U.S. ENVIRONMENTAL PROTECTION AGENCY
Office of Air and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, NC 27711
January 1 985
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DISCLAIMER
This report has been reviewed by the Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency, and approved for publication as received from TRC Environmental Consultants, Inc.
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 for commercial products constitute
endorsement or recommendation for use. Copies of this report are available from the National Technical
Information Service, 5285 Port Royal Road, Springfield, Virginia 22161.
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TABLE OF CONTENTS
SECTION NO. PAGE NO.
1.0 SUMMARY AND PURPOSE ..................... 1
2.0 BACKGROUND AND LITERATURE SURVEY .............. 5
2.1 EXPERIMENTAL STUDIES .................. 7
2.2 SURFACE MINE MODELS .................. 8
3.0 DESCRIPTION OF FIELD WORK
4.0 DATA REDUCTION ........................ 13
4.1 DATA REDUCTION OVERVIEW ................ 13
4.2 VCP OBSERVATIONS .................... 14
4.3 FIELD OBSERVER LOGS .................. 20
4.4 METEOROLOGICAL DATA .................. 20
4.5 CALCULATED PARAMETERS ................. 22
5.0 DATA ANALYSIS ........................ 39
5.1 COMPARISON OF IN-PIT VERSUS OUT-OF-PIT METEOPOLOGY ... 4C
5.2 COMPARISON OF OBSERVED FLOW PATTERNS AND METEOROLOGICAL
CONDITIONS ....................... 45
5.3 COMPARISON OF ESCAPE VELOCITY TO METEOPOLOGICAL
CONDITIONS ....................... 51
5.4 COMPARISON OF ESCAPE FRACTION AND METEOROLOGY ..... 56
6.0 SUMMARY OF FINDINGS ..................... 61
7.0 RECOMMENDATIONS FOR FUTURE WORK ............... 63
APPENDIX 1 BIBLIOGRAPHY ............ 1 ..... A-l
- 111 -
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1.0 SUMMARY AND PURPOSE
During the summer of 1983, Air Sciences, Inc. conducted a data
collection field study at four Western surface coal mines to characterize the
air flow within the mine pits. Smoke puffs were released at the bottom of the
pits, the motion of the smoke puffs was videotaped, and meteorological data
were collected both in and out of the mine pits during the smoke releases.
The field study was designed to allow visualization of airflow within the mine
pits.
TRC Environmental Consultants, Inc. contracted with FPA, OAQPS, to
reduce, quantify, interpret, and analyze the field data. The purpose of TRC's
work, which is described in this report, was three-fold:
• Reduce and translate the field data into a computer compatible data
base. This involved data averaging, computing some meteorological
parameters (e.g., stability classes), and data editing.
• Analyze and interpret the field data to investigate relationships
between in-pit and out-of-pit parameters, as well as other
calculated parameters such as escape fraction. This was
accomplished by deriving several representative parameters (exit
times, net exit velocities, escape fractions), and analyzing
statistical relationships.
• Perform a literature survey to identify previous investigations that
relate in-pit and out-of-pit emissions, and recommend future studies
that will better characterize pit retention. Pesults of the'
literature survey, and recommendations for future work, are included
in this report.
The data reduction effort, discussed in Section 4.0, utilized standard
National Climatic Data Center (NCDC) and FPA methods and conventions to
compute mean wind directions, wind speeds, and stability classes for each
smoke release episode. Additionally, data obtained from the VCR videotapes
were combined with pit geometry data to calculate average smoke puff exit
velocities for each release episode.
The smoke puff observations by themselves do not provide a quantitative
measure of particle pit retention. However, two independent techniques — one
based on a simple settling model, the other based on a particle deposition
model — were used in conjunction with assumed particle size distributions and
relevant field data to infer values of escape fraction.
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The analysis of the reduced and edited data base yielded several
findings:
• Computed escape velocities and escape fractions are lowest during
early morning, stable atmospheres, and during light wind speeds.
This finding is in agreement with observed flow patterns in the mine
pits, as the released smoke tracer was frequently observed to have
stagnated within the mine pits during these conditions. Conversely,
the greatest ventilation rates were observed during high wind speeds
and near neutral atmospheres. It should be noted that the nature of
the field study — visual observations of smoke puffs — precludes
observations at night when stable atmospheres are most likely to
occur. Hence the results of these investigations are biased toward
non-stable, daytime conditions.
• The computed escape velocity was found to be positively correlated
with measured wind speed, temperature, and wind direction, and
•negatively correlated with stability category, and the width of the
mine pit. However, when these parameters were used in linear,
multivariate regression analysis, only 32% of the variation in
escape velocity values could be accounted for. The linear model
could not be improved through the use of in-pit measurements rather
than out-of-pit measurements, or by stratifying the data by mine, by
stability class, or wind speed category. It is concluded that some
processes or variables, not accounted for in this analysis, must act
in conjunction with the above meteorological parameters to determine
the escape velocity
• In-pit winds are significantly different from out-of pit winds. The
in-pit wind direction differs from the out-of-pit wind direction by
about 60°. Further, no correlation between the in-pit versus
out-of-pit wind direction was found using linear regression
techniques, hence the in-pit wind direction can not accurately be
predicted from a knowledge of the out-of-pit wind direction. In-pit
wind speeds are, on the average, 25% smaller than out-of-pit wind
speeds. Linear regression analysis did identify a significant
positive correlation between in-pit and out-of-pit wind speeds.
• The value of escape fraction (that portion of the dust emitted in
the pit that leaves the pit) inferred from both the settling and the
deposition models is greater for unstable and neutral conditions
than for stable conditions, as shown in Table 1.1. This suggests
that stable atmospheres may suppress vertical motion causing
particulate matter to be retained in the mine pits. This finding is
in keeping with conventional understanding of atmospheric motion,
and agrees with the Winges model of pit retention which exhibits a
decrease in escape fraction with increasing stability.
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TABLE 1.1
ESCAPE FRACTION SHOWN BY STABILITY
PARTICLE SIZE SETTLING DEPOSITION WINCES FABRICK
DISTRIBUTION STABILITYC1) MODEL MODEL EQUATION EQUATION
UNIVERSAL
EDS
UNSTABLE
NEUTRAL
STABLE
UNSTABLE
NFUTRAL
STABLE
1.00
1.00
1.00
0.81
0.90
0.70
0.93
0.81
0.58
0.59
0.36
0.21
0.99
0.92
0.58
0.90
0.59
0.20
0.58
0.88
0.68
0.11
0.32
0.14
1. "A" stability class used for unstable; "D" used for neutral; "F" used for
stable.
• Reasonably good agreement is indicated between the escape fractions
inferred from the settling and deposition models and those predicted by the
equation proposed by Winges when the data are grouped by stability class.
Relatively poor agreement is indicated between escape fractions predicted by
the Fabrick equation and those inferred from the settling and deposition
models when the data are grouped by stability class.
• The magnitudes of escape fraction inferred from the settling model
and from the deposition model differ considerably, as shown in Table 1.1.
Because the field data provide no direct measure of escape fraction, it is not
possible to assess the accuracy of either the settling or deposition models as
means of computing escape fraction.
• Categorization of smoke release flows into characteristic patterns
indicates that the smoke puffs disperse within the pit before exiting during
stable, low wind speed conditions. This finding is as expected.
The literature search indicates that there are very few field studies
that have looked at pit retention or the relationship between in-pit and
•
out-of-pit concentrations. There are, however, several models that treat pit
retention and dispersion in pits, ranging in complexity from the simple Winges
and Fabrick algorithms to sophisticated numerical models that could be used to
simulate pit dispersion.
Because the few field studies that have attempted to quantify surface
mine pit effects to date have only inferred values of escape fraction, there
is a need to measure surface mine pit effects directly. Several direct
measurement investigations are proposed in this report. Additional effort in
deriving and validating simple escape fraction algorithms that can be
incorporated into regulatory dispersion models is needed.
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2.0 BACKGROUND AND LITERATURE SURVEY
host air quality dispersion models which are used to predict particulate
concentrations in the vicinity of surface mines simulate emissions as if they
were released at grade level. However, many different dust-producing
operations at open pit mines occur inside the pit, sometimes at depths of many
hundreds of feet below grade. It is reasonable to suspect that only a
fraction of the fugitive dust generated at the pit floor escapes to the
surface where it then may be transported to the mine boundaries. This
tendency for particulate matter to remain inside the pit has been called pit
retention.
There are probably two separate mechanisms that cause particulate matter
to be retained in a mine pit. The first is a de-coupling of the wind field in
the pit from the wind field at the surface, inhibiting or suppressing the
vertical transport of particulate from the bottom of the pit to the surface.
This suppression of vertical mixing is obvious to anyone who has viewed a deep
surface mine pit on a calm morning before sunrise. A shroud of diesel exhaust
often hangs in the pit, undisturbed by air movement at the surface. This pit
retention mechanism would be expected to be most pronounced during stable, low
wina speed conditions, such as occur at night. The other mechanism by which
particulate is retained is through deposition and settling at the mine pit
surface and along the pit walls. Both mechanisms occur simultaneously, and
they are linked. When the air in the pit is very stable, for example, the
residence time of a parcel of air in the pit increases, and the deposition and
settling processes have a longer time to act on airborne particulate.
Pit retention is a phenomenon that one would intuitively expect to occur
at a surface mine. Similarly, one would expect that the presence of the mine
pit itself would disturb the airflow over and inside the pit, so that the
"plume" of oust might not have the familiar Gaussian distribution imposed by
many dispersion models, or might have a significantly different trajectory
which would alter plume location. This altered plume shape or location, while
technically different than pit retention, is certainly a related issue. The
reason that these surface mine phenomena — pit retention and plume
perturbation — are of interest is because most air quality models neglect
them.
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Although both pit retention and plume perturbation will influence
particulate concentrations downwind of a surface mine, the nature of the
influence is different. Pit retention, which removes particulate from the air
before it leaves the mine pit, will always decrease downwind ambient
concentrations. If an otherwise accurate model simulation of a mine ignores
the influence of pit retention, and if there is indeed some retention of
particulate in the pit of the mine, then the model will overpredict downwind
concentrations. The error will be systematic and persistent. That is, even
in predicting long term (annual average) concentrations when the random
emission factor and modeling errors cancel out, the error due to neglect of
pit retention will still be present and will produce modeled concentrations
larger than those that would actually occur. On the other hand, model errors
caused by alteration of the plume shape or location (plume perturbation) due
to the presence of the pit could conceivably cause overpredictions or
underpredictions, depending upon how the pit is simulated. If the pit is
simulated as a very large area source, but the dust from the pit exits in a
narrow and coherent plume, then the maximum concentration at the top edge of
the pit could be underpredicted. Conversely, if the pit dust completely
disperses in the pit before exiting, a model that treats the pit as a small,
discrete area source may overpredict concentrations near the pit. However,
the effect of poor approximation of the mine pit source would be expected to
diminish with increasing time and with increasing distance from the pit. Over
long time intervals the plume of dust from the pit certainly approaches the
assumed Gaussian shape, and at large distances from the pit, the plume
perturbation caused by the pit might be expected to be insignificant.
The remainder of this section of the report examines previous, and in
one case proposed, investigations concerning pit retention and pit airflow. A
distinction is made between experimental studies of pit retention (of which
there are very few), and models of surface mine pits (of which there are
several). The discussion in this section is based on a literature review of
meteorological and air quality journals, as well as the authors' discussions
with investigators working with surface mine pits.
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2.1 EXPERIMENTAL STUDIES
Although field studies in the vicinity of surface mines have undoubtedly
been influenced by pit retention, very few studies have specifically addressed
pit retention. Furthermore, the recent interest in plume model validation
typified by EPA and EPP.I funded investigations (Bowne, 1982; Lavery, 1982),
has been confined to more conventional stack releases, and to date there have
been no rigorous validations of models applied at surface mines. There are
two reported studies in which the investigators detected discrepencies between
measured and modeled concentrations at surface mines, and attributed the
discrepencies to pit retention (Cole and Fabrick, 1984):
• WYOMING EMISSION FACTOR STUDY. In a year-long emission factor
development study conducted at two surface coal mines in Wyoming,
Shearer, et al (1981) derived site specific particulate emission
factors. These factors were then used in conjunction with mine
operational data to estimate the total particulate emission rate
from the pits. Independently, a modified PAL model (modified to
account for deposition over flat terrain) was used to backcalculate
from TSP concentrations measured at the edge of the pit to compute
"effective" emission rates from the pits. The two pit emission
rates differed by a factor of three, and Shearer, et al hypothesized
that only one-third of the particulate emitted in the pit was
escaping. It should be noted that the Wyoming study was not
designed to isolate the effects of pit retention, and the factor of
three difference in predicted emission rates could have been caused
by phenomena other than pit retention.
• BERKELEY PIT STUDY. Cole and Kunasz (1982) used a hybrid receptor
model to estimate effective particulate emissions at Anaconda's 550
meter deep copper pit in Butte, Montana. The receptor model
indicated that emissions from the pit, as detected by hi-vol
samplers at the pit perimeter, were 59 grams per second. A
conventional emissions inventory identified pit emissions at 125
grams per second. The authors hypothesized that only one-half of
the particulate matter emitted in the pit escaped to the surface.
As with the Wyoming emission factor study, the Berkeley Pit
investigation was not designed to look at pit retention, and it is
possible that the difference in calculated emission factors could
have been caused by emission factor errors or errors in the receptor
model.
The only field study that specifically examines pit retention and flow
fields at a surface mine appears to be the EPA funded work performed by Air
Sciences, Inc. (Hittman and Air Sciences, 1983), which provided the data
analyzed in this present report. The Air Sciences data collection effort is
outlined in Section 3.0 of this report.
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A number of studies in air pollution literature have looked at transport
and dispersion over features similar to surface mine pits, namely, urban
street canyons. The street canyon is like a mine pit in that both
configurations involve the release of pollutants at the bottom of a cavity,
and both must account for wind flow circulation within the cavity. The
particulate retention in a pit is slightly more involved than the dispersion
of gases in a street canyon because of additional effects of particulate
settling and deposition. A recent street canyon study includes: comparison of
an extensive date base of measured CO concentrations against predicted CO
concentrations from the Intersection Midblock Model (IMM) showing that the
model underpredicts frequently and severely (Zamurs and Piracci, 1982).
The wind tunnel offers a logical and convenient means of studying flow
over cavities. There has been only one reported wind tunnel study that
specifically looked at street canyons (Wedding, et al, 1977), and unfortu-
nately, that study considered shallow canyons, and did not incorporate the
findings into a dispersion modeling framework. A very ambitious wind tunnel
study has been proposed for funding by the Federal Highway Administration
(FHWA) in 1985. The FHWA wind tunnel study, which will consider street
canyons and deep cuts, could have direct applicability to the surface mine
pits. The goal of the 20 month effort will be to develop new, simplified
algorithms that can be incorporated into existing Gaussian highway models to
better predict pollutant concentrations. The FHWA study will investigate
neutral and stable atmospheres, will consider various wind directions and
surrounding roughness lengths, and will consider canyon width-to-depth ratios
that range from 6:1 to 1:6.
2.2 SURFACE MINE MODELS
There are two simple models which attempt to simulate pit retention by
deriving mass escape fractions:
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• FABRICK EQUATION. Beginning with the helical flow street canyon
equations used in the APRAC-1A model, Fabrick (1982) derived a pit
retention equation that depends on pit width and surface wind
speed. The Fabrick equation, discussed in detail in Section 4.5.6
of this report, predicted an escape fraction of 0.16 for the Wyoming
data discussed previously (Cole, and Fabrick, 1984).
• WINCES EQUATION. The ERTEC Mining Air Quality Model includes a
simple algorithm to account for bulk pit retention (Winges 1981).
The equation, which is discussed in detail in Section 4.5.7 of this
report, is a function of deposition-settling velocity, pit depth,
and vertical diffusivity. In an application of the Winges equation
to the Wyoming emission factor study mentioned previously, the
Winges equation predicted an overall escape fraction of 0.50, as
compared to the 0.33 escape fraction hypothesized from the Wyoming
data (Cole and Fabrick, 1984).
In the field of fluid mechanics, study of flow over cavities is a mature
discipline. Analytical solutions to the Navier-Stokes equations of fluid
motion are available if a number of simplifying assumptions are made (two-
dimensional, laminar flow, incompressible, constant diffusivity, flat plate
driven). As the simplifying assumptions are discarded, the simulation of
cavity flow becomes more complicated, and the Navier-Stokes equations can be
solved only with numerical analysis. A very recent investigation of cavity
flow was directed specifically at surface mine pits:
• HERWEHE MODEL. The Bureau of Mines funded development of a computer
simulation modeling scheme applicable to shallow surface mines
(Herwehe, 1984). The resulting modeling scheme simulates the
transport, diffusion, and dry deposition of fugitive dust emitted
from an idealized open-pit surface mine through the development of
two 2-dimensional finite-element models: a planetary boundary layer
model and an advection-diffusion model. The boundary layer model is
used to generate quasi-steady-state atmospheric flow fields and
diffusion quantities to be used as input data to the advection-
diffusion model, which simulates the ultimate fate of the non-
reactive particulate matter. Synoptic wind conditions, surface
roughness, complex nonhorizontal terrain, atmospheric stability, a
variety of pollutant sources, participate terminal settling and
deposition velocities, and particulate accumulation on the lower
surface are all factors accounted for in these two models. The
Herwehe model accounts for varying diffusivity with pit depth.
However, the model is hydrostatic (assumes constant pressure with
pit depth), and as a consequence would not be applicable to stable
atmospheres nor to pit walls with slopes greater than about 35
degrees from horizontal (Herwehe, 1984 a).
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The hydrostatic assumption in the Herwehe model may severely limit its
applicability to surface mine pits. Other non-hydrostatic models, not
specifically structured to simulate surface mines, could be modified to
acconiodate mine pits. Over the last eight years a series of Finite Element
Models (FEM) have been developed that could be modified for use with surface
mine pits:
• FEM. The 3-dimensional Galerkin Finite Element Model (FEM) was
developed by Lawrence Livermore Laboratory to simulate complex
terrains (Gresho, et al, 1976). The model has been applied to flow
over ridges, regional geostropbic flow, and heavy gas flow. The FEM
is non-hydrostatic, transient, and accepts non-constant
diffusivities. Finite element modeling can very easily accomodate
terrain features, such as a mine pit, and with much less difficulty
than finite difference models. The major drawback to the FEM model
is the large computer expense in running the model.
While numerical models such as Herwehe's or FEM provide scientifically
rigorous solutions to specific pit simulations, their use in regulatory
applications is limited because they have not been validated with field data.
Also, a good deal of effort is needed to prepare the numerical models for any
given simulation, and it is not a trivial matter to apply the same model to a
pit with an even slightly different geometry.
A brief bibliography of references that examine surface pit mine
modeling, pit retention, street canyons, flow over cavities, and related
issues is included in Appendix 1.
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3.0 DESCRIPTION OF FIELD WOPK
From June 28, 1983 through August 6, 1983, Air Sciences, Inc. conducted
smoke release measurements at four mines in Colorado, Wyoming, and Montana.
The smoke release field study, performed for the Industrial Environmental
Research Laboratory of EPA, is detailed in Studies Related to Retention of
Airborne Particulates in Coal Mine Pits — Data Collection Phase (Hittman and
Air Sciences, 1983), and is briefly summarized in this section.
The smoke release program was designed to provide data concerning air
motion within surface coal mine pits. At each of the four mines shown in
Table 3.1 smoke generators at the bottom of the pits were used to release
discrete 10 second puffs of diesel fuel smoke. An observer positioned at the
top of the pit recorded each smoke release on a video cassette recorder
(VCR). VCR recording began with the smoke release from the generator, and was
terminated when the smoke puff left the pit, or when the smoke in the pit
became so disperse that it was no longer visible. Roughly 8CO such smoke
release experiments, or episodes, were conducted at the four mines.
TABLE 3.1
STUDY MINES AND LOCATIONS
MINE LOCATION
Colorado Yampa Coal Company Steamboat Springs, CO
Caballo, Carter Mining Company Gillette, WY
Spring Creek Mining Company Decker, MT
P.osebud Coal Company Hanna, WY
Meteorological data were measured both in- and out- of the pits during
the smoke release experiments, and were recorded at one-minute intervals along
with date and time on cassette tape. The meteorological parameters are
indicated in Table 3.2
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TABLE 3.2
METEOROLOGICAL MEASUREMENTS
OUT OF PIT INSIDE PIT
6 m. ABOVE GROUND 3 m. ABOVE GROUND
WIND SPEED WIND SPEED
WIND DIRECTION WIND DIRECTION
TEMPERATURE STD. DEV. OF HORIZONTAL WIND DIRECTION
VERTICAL WIND SPEED TEMPERATURE
STD. DEV. OF HORIZONTAL WIND
DIRECTION
STD. DEV. OF VERTICAL WIND
SPEED
At the conclusion of each smoke release episode an observer located
outside the pit filled out a field data log, recording start time of release,
elapsed time until plume exit, distance to point of plume exit, direction to
exit, and in-pit temperature. In addition, he estimated and recorded cloud
cover and made a subjective assessment of smoke dispersion.
These data — the VCR cassettes, meteorological data tapes, and field
logs — form the experimental data base that is analyzed in this report.
12
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4.0 DATA REDUCTION
A major task in the analysis of the data collected by Air Sciences, Inc.
was the reduction of data into parameters, averages, distributions, and
expressions which allowed subsequent examination of air flow, meteorology, and
pit retention. This section explains which data were reduced, what methods
and conventions were used, and how the data were processed. A brief overview
of the data reduction effort precedes the discussion, with a description of
VCR observations, meteorological data, and special calculated parameters
following.
4.1 DATA PEDUCTION OVERVIEW
The data reduction effort began with the quantification of data from the
VCR cassettes. Video cassette recordings of the smoke release episodes were
analyzed to determine smoke puff exit times, and to categorize the smoke flow
into characteristic patterns. Additionally, mine identification and episode
case numbers were obtained from the VCR cassettes to allow synchronizing of
data.
Meteorological data gathered in the pit and out of the pit were recorded
on magnetic tape during the smoke release episodes. Generally, these data
consisted of one-minute averages of meteorological parameters.
Field logs, containing observations made on site, were also recorded on
magnetic tape. The observations included a qualitative estimate of initial
dispersion, time and angle of initial smoke puff exit, and episode case number.
Data from all three sources (VCR cassettes, meteorological data tapes,
and field logs) were merged into a single computer compatible data file. Data
were checked for consistent time synchronization, and anomalous or clearly
erroneous data were discarded. The data base was edited to remove illegal,
spurious characters which had been introduced during data transcription from
cassette to magnetic tape.
13
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The next step in the data reduction effort was to summarize data
corresponding to discrete smoke release "episodes." Each episode was an
individual smoke release experiment, that began with the smoke release from
the generators at the bottom of the pit, and ended when the smoke puff exited
the pit, or when the smoke puff became too dispersed to be visible. The
duration of the smoke release episodes ranged from about 30 seconds to more
than 5 minutes, but the average episode lasted about two minutes. Time
dependent data, such as wind speed and wind direction for example, were
averaged over the duration of the smoke release episode. Other parameters,
such as mean exit velocity and escape fractions, were computed for each smoke
release episode. The summarized data were written on a magnetic tape,
structured so that each smoke release episode was contained in one data
record. The data values in each smoke release episode record are shown in
Figure 4.1, and the derivation of each value is described in more detail in
sections 4.2 through 4.4 of this report. A FORTRAN program developed by TPC
was used to summarize the data which then were the basis for all subsequent
statistical analyses.
4.2 VCR OBSERVATIONS
As a preliminary step the videotapes were previewed in order to
facilitate extraction of data that would be used in the analysis. It was seen
from the previewing of the videotapes that some information still existed in
audio/visual form which would have to be translated to digital form for use in
the analysis. The name of the mine, the episode number (called "case number"
by Air Sciences), and many observations about weather or operation of the
smoke generators, were announced verbally on the VCR soundtrack. Furthermore,
it became evident that the behavior of the smoke releases could be grouped
into distinguishable patterns. The puff behavior in the pit appeared from
this previewing effort to take one of two forms - either it stayed in the pit,
or was ventilated out of the pit. In some cases it was not possible to
determine what the behavior was. When the puff stayed in the pit it did so
because it was recirculated back into the pit by an active circulation
pattern, or it simply did not move a significant distance and dispersed to the
point of losing visual definition while still within the confines of the pit.
14
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FIGURE 4.1
DESCRIPTION OF VARIABLES REDUCED
FROM SMOKE RELEASE EXPERIMENTS
VARIABLE
MINE
JDAY
NTIME
IDNUM
IRES1
IRES2
DESCRIPTION
MINE ID number
Julian day of experiment
Time of day (hr-minute)
Experiment case number
Time in seconds of
initial puff exit
Time in seconds of
exit of entire puff
VARIABLE
WDINN
AWSOUT
AWSIN
ATOUT
ATIN
NACOV
ISGOUT
DESCRIPTION
AWDIN normalized with respect to
pit long axis.
Average out-of-pit wind speed (MPH)
Average in-pit wind speed (MPH)
Average out-of-pit temperature (F)
Average in-pit temperature (F)
Average cloud cover (tenths)
Out-of-pit stability based on
NDUR
AWDOUT
AWDIN
WDOUTN
FRAC 1
FRAC 3
FRAC 5
Internal program
counter-number of
1-minute met. observations ISGIN
required to describe
experiment
Average out-of-pit
wind direction for
duration of experiment
Average in-pit wind
direction for duration
of experiment
AWDOUT normalized with
respect to pit long axis
Escape fraction based on
VEL1 and universal size
distribution
Escape fraction based
on VEL2 and universal
size distribution
Escape fraction based
on Winges equations and
universal size distribution
IPG
IVRT
AVRT
VEL1
VEL2
FRAC 2
FRAC4
FRAC6
sigma-theta (invalid)
In-pit stability based on sigma-
theta (invalid)
Stability based on Pasquill-Gifford
Stability based on sigma-w (invalid)
Average measured vertical velocity
(invalid)
Escape velocity based on IRES1
(cm/sec)
Escape velocity based on IRES2
(cm/sec)
Escape fraction based on VELl
and EDS size distribution
Escape fraction based on VEL2
and EDS size distribution
Escape fraction based on Winges
equations and EDS size distribution
15
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FIGURE 4.1 (continued)
VARIABLE DESCRIPTION VARIABLE DESCRIPTION
FRAC7 Escape fraction based FRAC8 Escape fraction based on Fabrick
on Fabrick equations equations and EDS size distribution
and universal size
distribution PITA Pit angle measured from North
WIDTH Pit width (meters) DEPTH Pit depth (meters)
ITYPE Observed flow pattern
category
16
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When the puff left the pit it appeared to do so under three different
regimes. In some cases there appeared to be a very local and subtle
circulation pattern caused by the sun heating the pit wall directly adjacent
to the puff and in turn circulating the puff up the wall and out of the pit by
a thermally driven circulation. In other cases the ambient wind was
sufficiently strong to advect the puff out of the pit. A third behavior was
seen where the putf simply lifted gradually from its point of formation as if
by thermal buoyancy. In some cases, however, it was not possible to determine
the behavior of the puff. During some smoke releases the puff became too
diffuse and lost visual definition which made it impossible to determine if or
when it left the pit. In other cases the Test Director erroneously terminated
the tape record of the experiment before it was possible to determine if the
puff had left the pit. A separate category to allow for these occurrences was
devised.
From these initial observations it was seen that determination of smoke
plume dimensions and smoke opacity could not be determined as had been
anticipated. There were several reasons for this. The smoke puff was not
often contained in a steady-state coherent plume that could be assigned
dimensions because the air motion in the pit was at times extremely chaotic.
Furthermore, the single camera viewing point located at the top of the pit
provided only a 2-dimensional view from the side, and there were no scale
features in the visual recording with which to judge distances. Smoke opacity
was also very difficult to judge because of the rapid movement of the smoke
plume and because of the varied color of the background. Even if it had been
possible to estimate opacity, this information alone, without optical path
length (i.e., plume width), would not have been sufficient to characterize
particulate concentrations.
In order to convert the audio/visual data into digital information that
could be analyzed, a data coding form was devised. The form was designed in a
way to allow the analyst to note the puff behavior in numerical code form and
thus translate the audio/visual data to digital characters. Factors noted on
the form included experiment identification, characteristic puff pattern (in
several optional forms), duration of the experiment and some general
observations which seemed to be important and might be amenable to further
17
-------
analytical treatment. A sample form is shown in Figure 4.2. The numerals
appearing under the headings "Question" and "Card Column" are solely for use
in keypunching the data. The elapsed time in Item 5 of the form is, in most
instances, the time from initial release of the smoke puff until the end of
the puff exited the pit. This time was determined by the VCP analyst using a
stopwatch.
When the smoke puff dispersed in the pit so completely that it was no
longer visible, the elapsed time was defined as the time from initial puff
release until the plume could no longer be seen. Defining elapsed time in
this manner introduces a bias in the measurement since the parcel of air in
which the smoke puff dispersed, in some cases, probably remained in the pit
even though it could not be seen. However, it was not possible to determine
how much longer the puff remained in the pit (because the puff could not be
seen), nor was it considered prudent to discard these episodes from the data
base (because this would completely ignore the longest duration smoke puff
retention episodes). The effect of this bias is to yield smaller smoke puff
retention times than would actually have been measured had the smoke puff been
visible, and for this reason the elapsed time associated with smoke release
episodes that dispersed completely within the pit must be considered a lower
bound. Of the roughly 800 individual smoke release episodes, smoke puffs from
248 of them dispersed completely within the pit. While this represents only a
fraction of the total smoke release episodes,. the bias is nevertheless
important because it is these long duration retention episodes that probably
account for the majority of pit retention.
•
The VCR data digitizing effort involved viewing the roughly 40 hours of
videotape while filling out the coding forms. One form was completed for each
smoke release episode, and then the data from the forms were keypunched and
merged into the reduced data base. The roughly 800 coding forms have been
bound in a separate volume.
18
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COAL MINE VIDEOTAPE CODING FORM
Figure 4.2
Card
Question Column
1. Mine Identification
1. Yampa
2. Caballo
3. Spring Creek
4. Rosebud
2. Case Number
3. Characteristic Pattern
A. Plume Stays in Pit
1. Recirculation evident
2. Disperses in pit
3. Other (explain)
BA Plume Exits Pit
1. Thermally driven up side wall
2. Advected out side wall
3. Exits center of pit
4. Other (explain)
C. Don't know
1. Sample invalid
2. Tape ends
3. Puff too diffuse
4. Other (explain)
4. General Observations
5. Elapsed Time (seconds)
1
2
o
3A
3B
3C
4
5
1
11
2
3
4
5
1
6
7
1
•
8
9
10
§S$XX&
11
12
13
*$
1
~7
3
1
Z-
1
x%*
If
1
1
I
$$
1
$
6
19
-------
4.3 FIELD OBSERVER LOGS
The Air Sciences, Inc. field observer logs were available on magnetic
tape. These data included smoke release episode number (case number), mine
name, date, release time of smoke puff, and duration of puff. In addition,
the observer estimated the qualitative degree of dispersion of the smoke plume
(digitized from 1 to 5), distance that the plume was in contact with the side
wall prior to exit, compass direction to exit location, cloud cover and
ceiling, and in-pit temperature.
These data were merged with the VCR and meteorological data to make up
the digital data base.
4.4 METEOROLOGICAL DATA
Meteorological data collected in and out of the mine pits were averaged
over the smoke puff episode in order to yield measurements of meteorological
parameters that influenced each smoke puff experiment. The methods used to
average wind speed, wind direction, Pasquill-Gifford stability class, sigma
theta stability class, and sigma w stability class are discussed in the
following sections.
4.4.1 WIND SPEED AND WIND DIRECTION
Individual one-minute wind speed values both in and out of the mine pits
were scalar averaged to yield mean wind speeds. That is, the sum of
individual wind speeds was divided by the total number of valid observations.
Individual one-minute wind directions both in and out of the mine pits
were unit vector averaged to yield resultant wind direction. Resultant wind
direction, WD , was given by:
WDr = arctangent (X/Y)
where
X = £sine (WD.)
Y =£cosine (WDp
WD. is individual wind direction
20
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4.4.2 PASQUILL-GIFFOFD STABILITY CLASS
The Pasquill-Gifford stability class was determined from cloud cover and
ceiling appearing in the field observer's logs, combined with average wind
speed during the smoke release episode. The procedures used to compute P-G
stability class were those used by the National Climatic Data Center (NCDC) in
deriving STAR data distributions.
4.4.3 SIGMA THETA
Values of sigma theta, that is, the standard deviation of horizontal
wind direction, were measured simultaneously inside and outside the mine
pits. Air Sciences programmed its Campbell Scientific CR-21 data logger to
print out sigma theta values once per minute, with a polling interval of 10
seconds and an averaging time of one minute (Cole, 1984). The sampling time
for the data logger is equal to the print out time. Clearly this choice of
time parameters was prompted by a desire to increase data resolution, and is
appropriate since some of the smoke releases were visible for only a minute or
so. However, the short (one minute) averaging time and the inability of the
data logger to accumulate sigma theta values longer than the print out time
means that some data manipulation is required to convert the one minute sigma
thetas to the fifteen minute sigma thetas needed to calculate atmospheric
stability class (Irwin, 1980). The sequence of consecutive one-minute sigma
theta values corresponding to each smoke puff episode were converted to
representative fifteen-minute sigma theta values using a method described by
Hanna (Hanna, et al, 1982). Next, the fifteen-minute sigma theta values were
categorized into alphabetic stability classes using procedures outlined by
Irwin (1980).
Examination of alphabetic stability classes determined in this manner
revealed that the vast majority of the stabilities were class "D", both in-
and out-of the pit. The Pasquill-Gifford stability distribution indicated
that only 10 percent of the episode hours were class "D", which is a much more
reasonable distribution for summer daylight hours. Given the low confidence
in the accuracy and representativeness of the sigma theta measurements,
stability classes determined by the sigma theta measurements were not used in
this study.
21
-------
4.4.4 SIGMA-W
Values of the standard deviation of vertical wind speed, sigma w, were
recorded by Air Sciences, Inc. at the out-of-pit sensor. It was hoped that
these measurements could be used as an indication of atmospheric stability
class by relating sigma w to sigma phi in the manner recommended by the AMS
(AMS,1977). However, TRC was notified by Air Sciences, Inc. that the sigma w
data were suspect and probably in error (Cole,1984). The error was attributed
to a faulty calibration of the vertical wind speed instrument that was
discovered by Air Sciences only after the data collection effort was
finished. Air Sciences did not know the direction nor magnitude of the
calibration error. The sigma w data were not used in this study.
4.5 CALCULATED PARAMETERS
Several parameters used in this evaluation of mine pit data were
calculated from direct measurements of observed values, from average
meteorological conditions, from approximations of actual pit geometry, or from
combinations of other measurements. These calculated parameters include:
• Pit dimensions. Obviously the geometry of the four mine pits is
somewhat complicated, yet the Fabrick and Winges escape fraction
equations require that the pits be assigned discrete widths,
lengths, and depths. Hence the mine pits have been "idealized" by
approximating their shapes with specific dimensions.
• Effective pit width. The path length from one edge of the pit to
the other, defined by the out-of-pit wind direction. This effective
pit width is used in Fabrick's escape fraction expression.
• Wind direction and pit orientation. In an effort to discriminate
out-of-pit wind directions with respect to the long-axis orientation
of the mine pits, wind directions were categorized as "parallel" or
"cross-wind" using a calculated variable "TACK".
22
-------
• Smoke puff escape velocity. The pit depth divided by the time
required for the smoke puff to exit the pit. This is a measure of
net upward vertical velocity.
• Smoke puff escape fraction determined by settling. The smoke puff
escape velocity minus settling/deposition velocity, weighted
according to particle size distribution. This escape fraction is
dependent upon assumed particle size distributions.
• Smoke puff escape fraction determined by deposition. The mass
fraction of particulate matter that remains in the dust plume at the
point of exit from the pit as determined by the Van der Hoven source
depletion/deposition model (Van der Hoven, 1968). This escape
fraction is dependent upon assumed particle size distributions.
• Fabrick escape fraction. The mass fraction of particulate that
escapes the mine pit as determined by Fabrick's equation. This
escape fraction is dependent upon an assumed particle size
distribution, wind speed, and pit width.
• Winges escape fraction. The mass fraction of particulate that
escapes the mine pit as determined by Winges' equation. This escape
fraction is dependent upon an assumed particle size distribution,
pit depth, and stability class.
Each of these calculated parameters is discussed in the following sections.
4.5.1 PIT GEOMETRY
In order to assign discrete values of pit depth, length, and width for
use in subsequent computations, it was necessary to use an idealized geometry
of each pit. Idealized pit dimensions are those identified in the final
report of the data collection phase of the pit retention study (Hittman and
Air Sciences, 1983), summarized in Table 4.1. Tests were conducted in two
different pits, or trenches, of the Yampa Mine, so that two different
geometries result.
23
-------
TABLE 4.1
PIT GEOMETRY
1.
2.
3.
4.
5.
MINE
YAMPA
6/28-6/30
YAMPA
6/30-7/2
CABALLO
SPRING CREEK
ROSEBUD
LENGTH
(m.)
600
600
850
1130
400
WIDTH
(m.)
22
40
408
40
40
DEPTH
(m.)
19.0
20.5
33.0
45.0
40.0
ORIENTATION TO
NORTH(a)
(deg)
35
55
90
110
130
a. Angle measured between North and long axis of rectangular pit.
As seen from Table 4.1, each of the pits approximates a rectangle, with
a length to width ratio ranging from 2 to 28.
4.5.2 EFFECTIVE PIT WIDTH
The effective pit width, that is, the path length from one side of the
pit to the other (measured parallel to the wind direction), is a function of
the actual pit width, pit length, and wind direction. If the wind direction
is perpendicular to the long axis of the pit then the effective pit width, is
equal to the pit length. An approximation to the effective pit width, W is
given by:
W = W/sin (theta) Equation 1.
Subject to W -c L
Where W is actual pit width
L is actual pit length
theta is the angle between the out-of-pit wind
direction and the long axis of the pit.
24
-------
Equation 1 is an approximation that introduces a maximum error in W of
about 10 percent within a wind direction band of + 3 degrees at the Caballo
Mine. At all other mines, and at all other wind directions, the error
introduced by the approximation will be less than 6 percent.
4.5.3 WIND DIRECTION AND PIT ORIENTATION
To examine the influence that out-of-pit wind direction and pit
orientation have on dependent variables, all wind direction/pit orientations
were divided into "parallel" or "crosswind" categories. When the out-of-pit
wind direction was equal to the long axis orientation of the pit +_ 45 degrees,
the wind orientation was deemed parallel; when the wind direction was
perpendicular to the long axis of the pit, + 45 degrees, the wind orientation
was deemed crosswind.
4.5.4 SMOKE PUFF ESCAPE VELOCITY
For each smoke release episode, two different (but related) smoke puff
exit times were determined from the VCR recordings and the field logs: the
initial exit time and, where appropriate, the final exit time. Dividing the
pit depths by each of these exit times yields a measure of maximum and minimum
escape velocity for the smoke puff.
One correction was made to the computation of escape velocity to account
for the initial plume rise from the smoke generator. The pit depths were
decreased by three meters, in accordance with Air Sciences' observations
(Hittman and Air Sciences, 1983):
"...The (smoke) generators, which imparted an initial horizontal exit
velocity of 3 meters per second to the smoke, were generally oriented
with smoke exiting downwind. Although entrainment was rapid, under calm
and stable atmospheric conditions the plume rose to about 3 meters above
ground before stabilizing..."
25
-------
The exit velocities computed from pit depth and exit time represent an
average upward velocity of the air in which the smoke puff is dispersed. The
snoke acts like a tracer to allow the observer to see the motion of a parcel
of air in the mine pit. Because the settling velocity of oil smoke is very
small (see below), the smoke probably works well as a tracer.
As discussed in section 4.2 of this report, smoke release episodes in
which the smoke puff dispersed completely within the pit before exiting were
assigned exit times equal to the time duration from puff release until the
puff was no longer visible. This procedure yields artificially small exit
times, and artificially large exit velocities, for episodes in which the smoke
puff dispersed within the pit so completely that it was not visible.
4.5.5 SMOKE PUFF ESCAPE FRACTION DETERMINED BY SETTLING
The vertical velocities computed by dividing the depth of each pit by
the elapsed time between smoke puff release and smoke puff exit represent an
escape velocity for smoke particles. That is, the computed vertical velocity
is characteristic of oil smoke particles, which are perhaps 0.03 to 1.0
microns in diameter, and have a maximum gravitational settling velocity of
0.01 centimeters per second (Lapple, 1961). Real particulate matter found in
surface coal mine pits certainly has a mass mean diameter much larger than
smoke particles, and has an appreciably larger settling velocity. The
difference in the observed behavior of the smoke puffs and the hypothesized
behavior of actual coal mine dust, then, could be attributed to the settling,
or deposition, of the coal mine dust. Subtracting the downward settling
velocity of coal mine dust from the upward escape velocity of the smoke puffs
will yield a net vertical velocity that should better characterize real coal
mine dust. Furthermore, if the net vertical velocity is upward then the dust
particle will be expected to escape from the mine pit, and if the net vertical
velocity is downward then the particle will be retained in the mine pit.
When this reasoning is applied to a distribution of particle sizes it
provides a simple means of assessing an overall escape fraction for the
particle size distribution in question. This is accomplished by dividing the
particle size distribution into categories, and computing the settling
velocity for each of the size categories. Next, each settling velocity is
26
-------
subtracted from the smoke puff exit velocity, and those particle size
categories which exhibit a net upward velocity are assumed to have escaped
from the pit. Summing the mass fractions of each size category that escapes
from the pit yields an overall escape fraction.
Clearly this computation depends upon the initial particle size
distribution that is assumed to characterize real coal mine dust. In this
computation, and throughout the remainder of this investigation, two separate
particle size distributions are adopted:
• UNIVERSAL SIZE DISTRIBUTION. The so-called "universal" particle
size distribution is one that was chosen in a previous EPA study of
western surface coal mines (PEDCO & TRC, 1982) to represent a
composite, or average, western surface mine. The universal size
distribution considers only particles equal to, or smaller than, 30
microns in aerodynamic diameter. The universal particle size
distribution is shown in Table 4.2, with mass fraction expressed
cumulatively. Table 4.2 shows both deposition and settling
velocities, indicating that deposition velocity is greater than
settling velocity for all particle sizes in the universal particle
size distribution.
• EDS SIZE DISTRIBUTION. As part of a privately funded Emission
Development Study (EDS) for surface coal mines in the Powder River
Basin, particle size distribution was determined from optical
microscopic examination of millipore filters (Shearer, et al,
1981). The EDS study considered all particles in the visible size
spectrum, from about 2 microns physical diameter to greater than 130
microns. The EDS particle size distribution is shown in Table 4.3,
with mass fraction expressed cumulatively.
For each of the particle size distributions, equivalent deposition and
settling velocities were estimated. Deposition velocities associated with
each size category were taken from curves presented by Hanna, et al, (1982),
with assumed surface roughness length (z ) of 1.0 centimeter, and particle
density of 1.0 grams/cm , These values are approximate choices of roughness
length and particle density that could be expected to occur at surface mines
(see Hogstrom, 1978). Settling velocities were calculated from Stokes'
equation, with appropriate corrections for particle density and shape factor
(Hanna, et al, 1982). These deposition velocities and settling velocities are
shown in Tables 4.2 and 4.3 for the universal and EDS particle size
distributions. In each case it is assumed that the larger of the two
velocities, deposition velocity or gravitational settling velocity, is the
dominant removal mechanism for any given particle size category. For every
27
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particle size category in the 0-30 micron universal distribution, the
deposition velocity exceeds the settling velocity (see Table 4.2), whereas in
the EDS distribution, which includes a much larger range of particle sizes,
gravitational settling is the dominant means of particle removal for particles
larger than about 40 microns physical diameter. This conclusion is consistent
with experimental data which suggest that for particles smaller than 40
microns in size, deposition dominates, whereas for particles larger than 40
microns, gravitational settling dominates (Hanna, et al, 1982).
Both Tables 4.2 and 4.3 express removal velocity (either deposition or
settling) and cumulative mass fraction as a function of particle size, so it
is a simple matter to plot mass fraction versus removal velocity. Figure 4.3
(applicable to the universal distribution), and Figure 4.4 (applicable to the
EBS distribution) show the graphs of particle mass fraction and removal
velocity. In Figure 4.3 curves of mass fraction versus removal velocity are
shown for both particle settling and particle deposition merely to illustrate
that deposition velocities exceed settling velocities for all particle sizes
in the universal particle size distribution. Each of these graphs, Figures
4.3 and 4.4, indicates what fraction of the total mass of particles exhibit
removal velocities smaller than specified values. For example, Figure 4.3
shows that, for the universal particle size distribution, 86 percent of the
total mass of particles have a removal velocity less than 3.0 cm/sec. If the
escape velocity at a mine pit were exactly 3.0 cm/sec, then Figure 4.3
suggests that 86 percent of the total mass of the universal particle size
distribution would escape. Figure 4.4 shows that, for the EDS particle size
distribution, 13 percent of the total mass fraction of particles have a
removal velocity less than 3.0 cm/sec. If the escape velocity at a mine pit
were exactly 3.0 cm/sec, then Figure 4.4 suggests that 13 percent of the total
mass of the EDS particle size distribution would escape. Figures 4.3 and 4.4
can be used to infer escape fractions in the following manner:
30
-------
MASS
FRACTION1
i.o ;
0.9
0.8
0.6
0.5
0.4
0.3
o.:
0.1
FIGURE 4o3
REMOVAL VELOCITY VERSUS MASS FRACTION
UNIVERSAL DISTRIBUTION
-Vf
settling
±
£
-r-
deposition
0.5
1.0 1.5 2.0
REMOVAL VELOCITY, cm/sec.
31
2.5
3.0
-------
FIGURE 4o4
REMOVAL VELOCITY VERSUS MASS FRACTION
EDS PARTICLE DISTRIBUTION
MASS
FRACTION
1.0
0.9
0.8
0.7
0.6
0.5 -
0.4
0.3
0.2
0.1
10 20 30 40
REMOVAL VELOCITY
50 60
, cm/sec.
32
-------
1. Choose a particle size distribution, either universal (0-30 microns)
or EDS (0-130 microns), of interest.
2. Calculate escape velocity for a given smoke puff episode by dividing
pit depth by smoke puff exit time, as discussed in section 4.5.4.
3. Enter Figure 4.3 for universal distribution, or Figure 4.4 for EDS
distribution, with escape velocity along the horizontal axis. Find mass
fraction along the vertical axis of the Figures. The mass fraction is
the amount of particulate that escapes.
In practice it would have been too time consuming to enter the graphs in
Figures 4.3 and 4.4 manually for each of the roughly 800 smoke release
episodes. To expedite the computation of escape fraction, the curves in the
Figures were approximated with analytical expressions (curve fits), and
computer programmed to calculate two escape fractions — one corresponding to
the universal size distribution and the other corresponding to the EDS
distribution — for each of the 800 smoke release episodes. The escape
fractions were stored on magnetic tape for subsequent analysis. The results
of this analysis are discussed in Section 5 of this report.
It is important to understand that the computation of escape fraction in
the manner described in this subsection is an oversimplification of the actual
pit retention phenomenon. The computation of escape fraction outlined in this
subsection balances upward escape velocity, determined from the smoke puff
behavior, against downward deposition and settling velocities, without regard
to the exact details of smoke plume trajectory or plume-ground interaction.
It is reasonable to expect that this simplification may tend to overestimate
the true escape fraction, because the actual deposition process is strongly
dependent upon the plume-terrain interaction when the plume is very close to
the pit floor and the pit walls. It may be that some of the particulate
matter released in the pit of a surface mine is removed by deposition at the
pit floor even when the net escape velocity (the upward exit velocity minus
the downward deposition or settling velocity) is directed upwards. The
magnitude of the overestimation of the escape fraction will be greatest for
small particles whose deposition velocities are consistently lower than the
upward exit velocity. Furthermore, as has been explained in section 4.5.4,
33
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there is a bias in the computation of escape velocity for some of the smoke
release episodes which yields artificially large exit velocities, and
correspondingly large escape fractions.
Because of the uncertainty and known biases in this method of inferring
escape fraction from the field data, a second, independent means of estimating
escape fraction was adopted. This second means of computing escape fraction
is discussed in the next subsection.
4.5.6 SMOKE PUFF ESCAPE FRACTION DETERMINED BY DEPOSITION
The previously discussed method of inferring escape fraction balances
upward escape velocity against downward particle removal velocity, and in so
doing ignores the plume-terrain interaction which influences particle
deposition. A simple model that accounts for both settling and deposition is
the so-called source depletion model (Van der Hoven, 1968), which is given by
dx 1 -(2/7T)1/2(Vd/u)
Equation 2
where
[xf dx 1
exp I
i Oz exp (h2/2oi) J
Q /Q is the ratio of apparent emission rate at distance x, divided by
X O
true emission rate at the source.
(j is the standard deviation of vertical concentration, m.
h is the separation distance between the dust plume and the
ground, m.
V is the larger of deposition or settling velocity, m/s.
u is wind speed, m/s
The value of Q /Q is, of course, equal to escape fraction.
X O
A simple FORTRAN computer program was written to solve Equation 2 by
stepwise numerical integration for both the universal and the EDS
distributions. The forward step size used in the integration was set to five
meters, and the plume-terrain separation distance, h, was arbitrarily set to
34
-------
one meter to simulate ground level sources within the mine pit. Values of
wind speed, effective pit width, and stability class determined from the field
data were substituted into equation 2. The wind speed for -each episode was
set equal to the horizontal wind speed measured inside the pit, and the values
of sigma-z were computed using the Martin (1976) curve fits to the familiar
Turner dispersion coefficients (Turner, 1970). The limit of integration, x,
was set equal to the effective pit width defined by the in-pit wind direction.
Equation 2 was solved for each particle size category (weighted by mass
fraction) for the universal and the EDS particle size distributions. This
computation was repeated for each of the roughly 800 smoke release episodes,
and the resulting escape fractions were stored on magnetic tape for subsequent
analysis. The results of this analysis are discussed in Section 5 of this
report.
There are several potential sources of error inherent in the use of
Equation 2 to infer escape fractions. The most important of these is the
specification of deposition velocity, which can vary by an order of magnitude
for small particles depending upon ground cover, roughness height, and other
parameters (Hanna, et al, 1982). Additionally, the source depletion model
imposes a Gaussian distribution in the vertical, and assumes that the
particulate concentration is depleted uniformly throughout the entire vertical
extent of the plume. The effect that these assumptions have on predicted
surface mine pit escape fractions is not known.
4.5.7 FABRICK ESCAPE FRACTION
Fabrick (1982) derived a mine pit escape fraction equation that depends
upon the width of the pit, the wind speed at the top of the pit, and a
particle size distribution:
= 1-Vd[C/u(i+ln4 )] Equation 3
where £ is escape fraction
u is wind speed, m/s
w is pit width, m
V^ is the larger of deposition or settling velocity, m/s
C is an empirical dimensionless constant with a value of 7.
35
-------
The Fabrick escape fraction was evaluated for both the universal and the EDS
particle size distributions for each of about 800 individual smoke release
episodes by substituting into Equation 3 values of effective pit width and
out-of-pit wind speed from the field data, and the larger of deposition and
settling velocity. The particle size distributions were subdivided into
discrete size categories so that individual escape fractions could be computed
fcr each size category. Multiplying these individual escape fractions by the
mass fraction for each size category, and then summing the product over the
entire particle size distribution, yielded escape fractions for each particle
size distribution. These computations were performed in a subroutine of the
data reduction computer program. The universal and the EDS particle size
distributions are shown in Tables 4.5 and 4.6. Note that the distributions
appearing in Tables 4.2 and 4.3 are identical to the distributions shown in
Tables 4.5 and 4.6, although the former distributions (Tables 4.2 and 4.3) are
expressed cumulatively with particle size, whereas the latter distributions
(Tables 4.5 and 4.6) are expressed by particle size category.
4.5.8 WINCES ESCAPE FPACTION
Winges (1981) developed an equation to calculate the particulate escape
fraction from surface mine pits. The escape fraction is given by:
K I H Equation 4
where e is the escape fraction
V, is the larger of deposition or settling velocity, tn/s
K is vertical diffi
z
E is pit depth, m.
2
K is vertical diffusivity, m /sec
Z
36
-------
TABLE 4.5
UNIVERSAL PARTICLE SIZE DISTRIBUTION — CATEGORIZED
DIAMETER (a)
RANGE
(microns)
0-2.5
2.5-5.0
5.0-10.0
10.0-15.0
15.0-20.0
20.0-30.0
MASS (a)
FRACTION
0.021
0.073
0.176
0.148
0.115
0.467
MID-POINT
DIAMETER
(microns)
1.25
3.75
7.50
12.50
17.50
25.00
DEPOSITION (b)
VELOCITY
(cm/sec. )
0.070
0.600
1.100
1.400
2.000
2.700
a. PEDCo & TRC, 1S82, p. 16
b. Hanna, et al, 1982, Fig. 10.4, p.70 @ z<
1 cm., density = 1.0 g/cc
The Winges equation was also evaluated for both the universal and the
ECS particle size distributions for each individual smoke release episode.
Values of stability class and pit depth were determined from the field data.
Values of vertical diffusivity corresponding to stability class were those
presented by Draxler (1977), shown in Table 4.7.
TABLE 4.7
VERTICAL DIFFUSIVITY, m2/sec
P-G STABILITY
KZ
A
50
B
30
C
15
D
7
E
3
F
1
37
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5.0 DATA ANALYSIS
The substantial data base collected both during the original smoke
release experiments and from analysis of the video tape recordings of those
experiments provided the basis for the statistical comparisons described in
this section. The data consist of meteorological data, collected both in and
out of the four mine pits, escape times of the smoke puffs within each of the
pits, and discrete categories which describe the movements, or flow patterns,
of the puffs within those pits.
Several different types of comparisons are provided from analysis of
these data. Out-of-pit meteorological data were correlated with the
meteorological measurements performed at the same time within the mine pits.
In addition, the out-of-pit meteorological data were compared to the flow
pattern categories, to determine the relationships between the observed puff
movements and conditions occurring outside of the pit. The escape velocities
of the smoke puffs described in Section 4.5.4 were then correlated with the
meteorological measurements to determine if a predictive method could be
developed. Finally, escape fractions were computed and compared to escape
fractions predicted by existing equations.
The statistical tests or methods used to facilitate the above-described
comparisons varied depending on the type of data being used. Single variable,
linear regression analysis and direct comparisons of mean values were
performed on the meteorological measurements from in and out of the mine pits
to determine not only relationships between the two data sets, but also to
ascertain if in-pit conditions could be predicted from knowledge of out-of-pit
conditions. Comparisons of flow patterns to meteorological measurements were
facilitated through the use of frequency distributions, given the fact that
these patterns are described by discrete, and arbitrarily defined,
categories. Finally, the escape velocities developed from the smoke release
observations were compared to meteorological measurements using both a
comparison of mean values, and through multi-variate linear regression
techniques. A predictive equation which relates the escape velocity to each
observed meteorological parameter was developed and tested using these
techniques.
39
-------
The "Statistical Analysis System" (SAS) computer package was employed
for all statistical comparisons described in this section. It provides means
for not only analysis but for data programming, storage and retrieval, and
file manipulation.
5.1 COMPARISON OF IN-PIT VERSUS OUT-OF-PIT METEOROLOGY
A comparison of mean values of meteorological parameters measured both
in and out of the mine pits is presented in Table 5.1. The means were
computed for all data combined, for each mine individually, for each stability
class, and for each wind speed category.
Variations in wind direction between the inside and outside of the pit
have been quantified by taking the difference between the two values (wind
direction (in) - wind direction (out), not to exceed 180°) for each smoke
release experiment. From Table 5.1, in-pit wind directions varied from those
measured out-of-pit by almost 60° for all data combined. Measurements
performed at the Rosebud Mine varied the most — about 87°, while those from
Carter's Caballo Mine varied the least — 36°. Trends in wind direction
differences between the inside and outside of the mine pits are not
discernible when compared by Pasquill-Gifford stability categories. However,
when viewed by wind speed categories, a very marked pattern emerges.
Consistently greater differences between in-pit wind directions and out-of-pit
wind directions occurred during light wind speeds. In fact, such variations
are two to almost four times greater during the lightest wind speeds (less
than 4 mph) than during the high speed categories (greater than 12 mph). This
result is not unexpected as wind directions are typically more variable during
light winds versus high winds, so comparisons of measured directions from two
different locations, even in close proximity, are usually not favorable.
However, these results do indicate that wind directions within a mine pit
probably cannot be well represented by measurements performed outside of the
pit. This finding will be explored further later in this section.
40
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41
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Average wind speeds occurring inside and outside the mine pits are also
listed in Table 5.1. Averages were computed for all data combined, as well as
for each mine, stability class, and wind speed category. Since the
categorization of wind speeds was accomplished using out-of-pit wind speeds,
the mean out-of-pit values listed by category represent essentially the
midpoints of those categories. However, comparing these means to those
occurring within the pit is nevertheless instructive.
From Table 5.1, it is evident that in-pit wind speeds are consistently
less than those occurring out-of-pit at the same times. This is true
regardless of which mine is analyzed, and is essentially true for all
stability categories as well. The one exception to this observation is during
the most unstable — Class A — conditions, where only 15 observations were
available for analysis. The largest differences between wind speeds measured
in and out of the mine pits occurred under the highest wind speed categories.
Measurements of sigma-theta atmospheric stability both in and out of the
mine pits were performed during the original smoke release experiments.
Comparisons of such measurements would be useful, however, examination of the
data revealed that the vast majority of the stabilities were class "D", both
in and out of the pit. The Pasquill-Gifford stability distribution indicates
that only 10 percent of the episode hours were class "D", which is a much more
reasonable distribution for summer daylight hours. Given the low confidence
in the accuracy and representativeness of the sigma-theta measurements,
comparisons of in-pit versus out-of- pit stability categories thus determined
are not provided.
Linear regression analysis was employed to determine if in-pit
meteorological conditions could be accurately predicted from observations
performed outside the pit. Regression analysis essentially determines the
"best fit" of a linear expression of the form:
where y is the dependent variable (or in-pit measurement), X is the
independent variable (or out-of-pit measurement), b is the y-intercept of
the "best-fit" line determined using the least-squares method, and b, is
slope of the best fit line.
42
-------
A correlation or relationship between the dependent (y) and independent (X)
variables is indicated if the slope of the "best-fit" line is not equal to
infinity or zero. A measure of the "goodness-of-fit" of the linear
expression, or model, to the observed data is described by the variation of
the actual measurements from the "modeled" values. This variation is usually
2
expressed in terms of the r parameter — a dimensionless number which
2
ranges in value between 0 and 1. The larger the value of r , the better the
model's fit.
Linear regression analysis was applied to determine whether or not the
wind direction within a mine pit could be determined from a knowledge of the
wind direction outside of the pit. Because of the discontinuity represented
by north winds (0° versus 360°), out-of-pit wind directions (WD) were
expressed in terms of the long axis of the pit by defining a variable called
"TACK" constrained to values between 0 and 90 degrees:
• 0 < WD < 90; TACK = WD
• 90 < WD < 180; TACK = 180-WD
• 180 < WD < 270; TACK = WD-180
• 270 < WD < 360; TACK = 360-WD
The variable TACK then represents the relation of the wind direction to the
long axis of the pit. A value of TACK equal to 0 represents a wind parallel
to the long axis of the wind, while a value of 90° represents a wind
perpendicular to the pit's long axis, or a crosswind. To avoid the northwind
discontinuity in the in-pit wind directions, the variable TACK was correlated
with the difference (AWD) between the in-pit wind direction and the out-of-pit
wind direction as used previously in Table 5.1. The results of the regression
analysis:
where
and
y
y
X
= b +
o
= AWD
= TACK
are shown in Table 5.2 The model was evaluated for all data combined, for
each mine individually, and for each stability class. The Student's - T test
was applied to evaluate whether the slope, b.. , of the best-fit line is
43
-------
different from zero (ie., whether y is a function of X). The parameter listed
as PR T in Table 5.2 evaluates whether or not the computed slope, b.. , is
statistically different from zero; values of PR T that approach zero indicate
good correlation between y and X, whereas values that approach unity represent
poor correlation.
TABLE 5.2
RESULTS OF REGRESSION ANALYSIS FOR WIND DIRECTION
MODEL
PARAMETER
ALL
b0 62.3
t>i 0.1
PR T .2248 .
r2 .002 .
DATA BASE
MINE
1
46.6
0.4
0091
063
2
14.2
1.3
.0001
.441
3
68.3
-0.5
.0017
.050
4
106.7
-0.9
.0001
.216
5
44.7
0.9
.0001
.284
1
36.5
0.5
.1340
.164
P-G STABILITY
2
69.1
-0.1
.7055
.001
3
57.5
-0.2
.1193
.007
4
27.7
0.5
.0081
.091
6
108.9
-0.7
.0528
.063
The results in Table 5.2 indicate that the out-of-pit crosswind angle,
TACK, is a poor predictor of WD, the difference between in- and out-of-pit
wind directions. In other words, the out-of-pit wind direction is a poor
predictor of the in-pit wind direction. For all data combined, the slope of
the regression line can be considered different from zero only at a confidence
level of about 78% (from PR T, 1.0 - .2248 = .7752). Hence variations in
out-of-pit wind directions characterize less than 1% of the variation of
2
observed in-pit variations (from r , .002 x 100 = 0.2%). The regression
model is improved somewhat when the data are separated for each mine, but even
at Mine 2 (Yampa, location 2), only 44% of the variability of in-pit wind
directions can be explained. Stratification of the data by stability class
does not improve the fit of the model either. From this analysis, it can be
concluded that in-pit wind directions can not be adequately predicted from
knowledge of out-of-pit wind directions.
A single variable linear regression analysis was performed to determine
if in-pit wind speeds are related to wind speeds measured out-of-pit. For
this test, the out-of-pit speed was used as the independent variable — X,
while the in-pit wind speed was used as the dependent variable — y. The
results of this investigation appear in Table 5.3.
- 44 -
-------
TABLE 5.3
RESULTS OF REGRESSION ANALYSIS FOR WIND SPEED
MODEL
DATA BASE
PARAMETER MINE
bo
bl
Pte-T
r2
ALL 1 2 3 4 5 1
1.5 2.0 3.6 -0.1 2.2 -0.5 0.9
0.6 0.6 0.4 0.7 0.4 0.9 1.0
.0001 .0001 .0001 .0001 .0001 .0001 .0008
.61 .55 .58 .73 '.34 .75 .60
P-G STABILITY
2 346
1.1 1.2 -0.4 1.1
0.8 0.6 0.7 0.4
.0001 .0001 .0001 .0003
.56 .53 .63 .19
The results listed in Table 5.3 indicate that the out-of-pit wind speed
is a reasonably good predictor of the in-pit wind speed. For all data
combined, the small value of PR=»T indicates that the dependent variable is
2
strongly correlated with the independent variable. Further, the r value of
0.61 indicates that 61% of the variation of the in-pit wind speed is explained
by the regression model. Stratification of the data by mine, and by stability
class yielded essentially similar results. An interesting exception occurs
when the wind speeds are analyzed during stable atmospheres (P-G class 6).
For these conditions, even though a strong correlation is indicated by the
Student's T test, only 19% of the variation of the in-pit wind speed can be
explained by the model. It is not clear why the model's performance is
reduced during these conditions, but it is proposed that conditions within the
mine pit are sometimes decoupled from the atmosphere above during stable
conditions.
5.2 COMPARISON OF OBSERVED FLOW PATTERNS AND METEOROLOGICAL CONDITIONS
The videotape recordings of the actual smoke release experiments were-
reviewed to determine not only the smoke puff escape time from which escape
velocities were computed, but also to determine the characteristic patterns of
the puff's transport and dispersion within the mine pits. The categorization
of the observed flow patterns was accomplished through the use of the coding
forms described in Section 4.0 of this report.
45
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The discrete categories obtained from analysis of the data contained on
the coding forms are as follows:
TABLE 5.4
CATEGORIZATION SCHEME USED TO DESCRIBE PUFF BEHAVIOE
PUFF REMAINS PUFF EXITS
IN PIT PIT DON'T KNOW
100-recirculation 10-puff thermally up 1-invalid sample
evident sidewall
200-puff disperses 20-puff mechanically 2-videotape ended
in pit driven up sidewall prematurely
30-puff exits at center 3-puff too diffuse
of pit
Definitions for each of the above categories were provided in Section 4.0. It
should be noted however that two of the categories do not appear in the
analysis. Samples considered by the observer to be invalid (Category 1), 43
out of the original 811 puff releases, were extracted from the data base
before analysis. Also, puffs considered to have escaped the pit by thermal
processes (Category 10) were combined with those driven mechanically up the
side wall (Category 20) as the two categories were virtually
indistinguishable. Undoubtedly both processes must occur within mine pits,
however, it was found to be impossible to distinguish between them from the
available visual record.
Data which are comprised of discrete, arbitrarily defined categories,
such as the flow patterns described above, do not lend themselves to the types
of statistical analysis used for other parameters. It is meaningless to
compute an "average" flow pattern, just as it is impossible to state that a
flow pattern is proportional to (or correlated with) another parameter.
However, it is instructive to identify the categories which occur most
frequently, and to examine the frequency of occurrence as a function of
meteorological conditions. For this reason, the comparison of observed flow
patterns to measured meteorological conditions is comprised of comparisons of
46
-------
the frequency of occurrence of specified flow patterns for given
meteorological parameters. These frequencies, expressed as percentage of all
occurrences of a given meteorological condition, are listed in Tables 5.5a
through 5.5c, along with total count of occurrences. Every vertical column of
frequencies in Table 5.5 sums to 100 percent, so that each individual entry in
the Table represents the frequency of occurrence of a specified flow category
observed during a specified meteorological condition. For example, of all the
smoke release episodes during which the P-G stability class category was 1
(left most vertical column of values under heading P-C STABILITY 1), 27
percent of the episodes exhibited puff dispersion while the puff remained in
the pit. Similarly, of all the smoke release episodes during which the
out-of-pit wind direction was cross-wind to the long axis of the pit (right
most vertical column of Table 5.5c), 5 percent of the episodes exhibited puff
recirculation.
From Table 5.5, the observed flow patterns appear to relate better to
atmospheric stability categories than to either the wind speed categories, or
to wind directions. For example, during the most unstable conditions — P-G
Class 1 — 53% of the smoke puffs were observed to exit the mine pit, while
only 27% remained in the pit. In contrast, during stable conditions — P-G
Class 6 — only 16% of the observed puffs exited the pit while 58% remained in
the pit. However, the distribution of flow patterns remains reasonably
constant for different wind speeds, and for different wind directions, at .
least in terms of the primary categories (ie., "plume remains in pit" versus
"plume exits pit").
•
Other observations are possible from examination of the sub-categories
of flow patterns. Recirculation of smoke puffs were observed more frequently
during neutral atmospheres (P-G Class 4), and during the highest wind speeds
(wind speed Category 5) than during other stability and wind speed
categories. Evidently the recirculation patterns observed in the mire pits
were most frequently associated with aerodynamic wake effects (ie., smoke
puffs trapped within an aerodynamic cavity formed alongside the upwind
sidewall). Another anticipated recirculation pattern, cellular circulation
structures caused during light winds by differential surface heating or
cooling, were also observed but much less frequently.
47
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TABLE 5.5a
PERCENT FREQUENCY OF OCCURRENCE AND TOTAL NUMBER
OF OCCURRENCES (in parentheses) OF OBSERVED FLOW
PATTERNS FOR GIVEN METEOROLOGICAL CONDITIONS
FLOW CATEGORY
P-G STABILITY
2 3
PUFF REMAINS IN PIT
100-RECIRCULATICN
200-DISPERSED
CAT. TOTAL
0 ( 0)
27 ( 4)
27 ( 4)
5 ( 13)
25 ( 65)
30 ( 78)
2 ( 8)
32 (109)
34 (117)
11 ( 8)
33 (25)
44 (33)
4 ( 2)
54 (35)
58 (37)
PUFF EXITS PIT
20-EXITS SIDE WALL 20 (3) 22 ( 57) 23 ( 79) 18 (14)
30-EXITS CENTER 33 ( 5) 11 ( 29) 2(8) 0(0)
CAT. TOTAL 53 (8) 33 ( 86)
12 ( 8)
4 ( 2)
25 ( 87) 18 (14) 16 (10)
DON'T KNOW
2-TAPE ENDED
3-TOO DIFFUSE
CAT. TOTAL
0 ( 0)
20 ( 3)
20 ( 3)
3 ( 7)
35 ( 92)
38 ( 99)
3 ( 11)
37 (127)
40 (138)
0 ( 0)
38 (29)
38 (29)
0 ( 0)
26 (17)
26 (17)
Column total
100 (15) 100 (263) 100 (342) 100 (76) 100 (64)
(760 Valid P-G Stability Class Observations)
48
-------
TABLE 5.5b
PERCENT FREQUENCY OF OCCURRENCE AND TOTAL NUMBER
OF OCCURRENCES (in parentheses) OF OBSERVED FLOW
PATTERNS FOR GIVEN METEOROLOGICAL CONDITIONS
FLOW CATEGORY
WIND SPEED CATEGORY
2 3
PUFF REMAINS IN PIT
100-RECIRCULATION
200-DISPERSED
CAT. TOTAL
2 (
39 (
41 (
I 76)
: 80)
5
29
34
(
(
(
9)
54)
63)
2 (
30 (
32 (
: 5)
: 77)
: 82)
9 (
26 (
35 (
10)
30)
40)
7
17
34
( 1)
( L)
( 2)
PUFF EXITS PIT
20-EXITS SIDE WALL 18 ( 35) 23 ( 42) 23 ( 59)
30-EXITS CENTER 9 ( 18) 10 ( 19) 3(8)
CAT. TOTAL 27 ( 53) 33 ( 61)
22 (26)
0 ( 0)
33 ( 2)
0 ( 0)
26 ( 67) 22 (26) 33 ( 2)
DON'T KNOW
2-TAPE ENDED
3-TOO DIFFUSE
CAT. TOTAL
4 ( 8)
28 ( 55)
32 ( 63)
3 ( 6)
32 ( 59)
35 ( 65)
1 ( 3)
42 (109)
43 (112)
3 ( 3)
41 (47)
44 (50)
0 ( 0)
33 ( 2)
33 ( 2)
Column total
100 (196) 100 (189) 100 (261)
(768 Valid Wind Speed Observations)
100 (116) 100 ( 6)
49
-------
TABLE 5.5c
PERCENT FREQUENCY OF OCCURRENCE AND TOTAL NUMBER
OF OCCURRENCES (in parentheses) OF OBSERVED FLOW
PATTERNS FOR GIVEN METEOROLOGICAL CONDITIONS
FLOW CATEGORY
PARALLEL
OUT OF PIT WIND DIRECTION
CROSS-WIND
PUFF REMAINS IN PIT
10C-RECIRCULATION
200-DISPERSED
CAT. TOTAL
PUFF EXITS PIT
20-EXITS SIDE WALL
30-EXITS CENTER
CAT. TOTAL
Column total
3 (
32 (
8)
90)
35 ( 98)
22 ( 62)
9 ( 25)
31 ( 87)
5 ( 24)
30 (146)
35 (170)
21 (102)
3 ( 15)
24 (117)
DON'T KNOW
2-TAPE ENDED
3-TOO DIFFUSE
CAT. TOTAL
2 ( 6)
32 ( 90)
34 ( 96)
3 ( 15)
38 (185)
41 (200)
100 (281)
(768 Valid Wind Direction Observations)
100 ( 487)
- 50
-------
To summarize the findings of this investigation of characteristic flow
patterns it was determined that:
• smoke puffs remained in mine pits more often during stable
atmospheres than during unstable atmospheres;
• smoke puffs remained in mine pits more often during light wind
speeds than during high wind speeds;
• recirculating puffs are mostly associated with "downwash" cavities
formed alongside the sidewalls during high winds;
• puff exits at the center of the pit (as opposed to exits along
sidewalls) are most frequently associated with very unstable
atmospheres and light wind speeds;
• circulation patterns exhibit a greater association with stability
categories than with wind speed categories or with wind directions.
5.3 COMPARISON OF ESCAPE VELOCITY TO METEOROLOGICAL CONDITIONS
The videotape recordings of each smoke puff release were analyzed to
determine the amount of time, termed retention time, required for the smoke
puff to escape the pit. Given the depth of the pit and the retention time,
the upward escape velocity was computed for each observation. This velocity
was then compared to coincident meteorological conditions measured both in and
out of the mine pit. The techniques used to make these comparisons are
similar to those described previously in this section — namely, comparison of
mean values, and regression analysis.
Average escape velocities were computed for all data combined, as well
as for each mine individually, each stability category, and each wind speed
category. These results are presented in Table 5.6.
51
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TABLE 5.6
AVERAGE ESCAPE VELOCITY AS A FUNCTION
OF METEOROLOGICAL PARAMETERS (a)
DATA BASE
ALL
MINE= 1
MINE= 2
MINE= 3
MINE= 4
MINE= 5
ESCAPE DATA BASE
VELOCITY
53.
40.
50.
59.
49.
65.
4 STAB= 1
3 STAB= 2
6 STAB= 3
5 STAB= 4
0 STAB= 6
9
ESCAPE DATA BASE ESCAPE
VELOCITY VELOCITY
39.6 WS. CAT.= 1 34.3
48.4 WS. CAT.= 2 47.2
61.5 WS. CAT.= 3 65.3
54.2 WS. CAT.= 4 67.5
30.6 WS. CAT.= 5 74.2
velocity expressed in cm/sec.
From Table 5.6 it is seen that the computed escape velocity varies for
each mine, stability category, and wind speed. The average escape velocity
was found to be much higher for Mine 5 (Rosebud) and Mine 3 (Caballo) than
for the other mines tested. From the configuration of the mines tested it is
unclear why the escape velocity for Mine 5 (Rosebud) should be higher than for
the other mines. The Rosebud mine pit is about as wide as it is deep, as are
the Yampa (.at Location 1), and the Spring Creek mines (see Table 4.4). If the
mine configuration were the only factor affecting the escape velocity, then
the values from these three mines should be roughly the same. Evidently the
mine configuration is only one of the factors which determine the escape
velocity.
The distributions of escape velocities as functions of stability class
and wind speed are more in line with expected results. Average escape
velocities were found to be highest during neutral and near neutral
atmospheric stabilities (P-G Classes 3-4), and lowest under stable
conditions. Consistent with these results, higher escape velocities occurred
during high wind speeds, than during low wind speeds.
52
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It is likely that the out-of-pit wind speeds are, in fact, the most
significant factor in determining the escape velocity. The stability
categories are themselves a function of wind speed. Both the most stable and
the most unstable categories occur during light wind speeds, while near-
neutral conditions are associated with high wind speeds. Since the escape
velocities during the most unstable categories (P-G Class 1) are less than
during neutral conditions, even though unstable atmospheres enhance vertical
movements, it is likely that the magnitude of the wind speed is more important
in determining the escape velocity than is the thermal stratification
described by the stability category.
The importance of mine configuration and meteorological conditions in
determining the escape velocity can be quantified more readily using
regression analysis. It is apparent that this velocity is controlled by a
variety of factors, so the most appropriate analysis tool for evaluating these
effects simultaneously is linear, multi-variate regression analysis. This
sophisticated statistical approach is available on the SAS computer package.
A linear model constructed using a multivariate regression approach
takes the form:
y = b + b,X1 + b0X0 + b,X. ... b X
o 11 2. 2. JJ nn
where y is the dependent variable, b is the intercept of the best fit
"line", and b, through b are regression parameters, equivalent to the
slope of the line in single variable regressions, for independent variables
X^ through X . Interpretation of the results of multivariate regressions
is similar to that described previously for single variable linear
regressions. The regression parameters, b.. through b , should be
significantly different from zero, as indicated by the Student's-T test, for
2
significant correlation, and the r value, a measure the model's "fit",
should approach unity for a good-performing regression model.
Two basic models were tested using multivariate regression techniques.
The first was based on meteorological measurements performed outside of the
nine pit, and the second was based on in-pit measurements. Table 5.7
describes the variables used in each of the models, while Table 5.8 lists the
results of the regression analysis.
- 53 •
-------
TABLE 5.7
PARAMETERS USED IN MULTIVARIATE REGRESSION ANALYSIS
DEPENDENT
MODEL Y
1 ESCAPE
VELOCITY
2 ESCAPE
VELOCITY
INDEPENDENT
Xl
WIND SPEED
(out)
WIND SPEED
tin)
X2
TEMPERATURE
(out)
TEMPERATURE
(in)
X3 X4
TACK P-G
STABILITY
TACK P-G
STABILITY
X5
WIDTH
WIDTH
The reader is reminded that the variable TACK represents the angle of the wind
direction with respect to the long axis of the mine pit. Angles approaching
zero represent winds that are parallel to the long axis of the pit, while
angles approaching 90° represent crosswinds perpendicular to the long axis.
TABLE 5.8
RESULTS OF MULTIVARIATE REGRESSION ANALYSIS
NO.
MODEL SAMPLES bo PR>T
1 634 3.41 2.88
.0001
2 543 -6.27 3.94
.0001
b2
PR>T
0.48
.0001
0.47
.0001
b3
PR=-T
0.11
.0001
0.10
.0002
b4
PR^T
-3.49
.0001
-0.94
.0949
b5
PR>T
-0.01
.0154
-0.01
.2040
r2
0.32
0.32
The results indicate that neither of the models tested can adequately
predict the escape velocities computed from the smoke release experiments.
Regardless of whether in-pit or out-of-pit meteorological parameters are
employed, only 32% of the variability of the escape velocity can be explained
by the models. This is not to say that the independent parameters tested are
not correlated with the escape velocity. In fact, significant correlations
were found between the escape velocity and the measured wind speed, both in
and out of pit, the temperature, both in and out of pit, the angle of the wind
54
-------
TACK, and P-G stability category (a negative, but still significant
correlation). The weakest correlation between the escape velocity and one of
the independent variables was demonstrated for the mine width. When out-of-
pit measurements are used, the width was found to be significantly, but
negatively, correlated with the escape velocity at the 98% confidence level.
However, when in-pit measurements are used, the confidence level for
significance drops to 80%. Given the fact that all the independent variables
tested demonstrate some degree of correlation with the escape velocity, and
yet the model only accounts for 32% of the variation of the velocity, clearly
some, as yet unaccounted for, factors or variables must influence the escape
velocity.
In an effort to improve the performance of the model, several iterations
of the multivariate regression techniques were performed. The above two
models were modified, by eliminating the variable WIDTH from the analysis,
without a meaningful improvement in the results. The same two models were
also evaluated by restricting the data base to data from each mine, and each
stability class, again without improvement. The results of each of these
investigations is included as Volume 2 of this report.
In summary, the escape velocity has been found to be positively
correlated with wind speed, temperature, and wind direction, and negatively
correlated with stability category and the width of the pit. However, these
parameters, when used in a linear regression model, do not provide very good
predictions of the escape velocity. Only 32% of the variability of the escape
velocity can be explained by these parameters. Certainly other processes must
act, in conjunction with the variables tested here, to determine the escape
velocity.
Perhaps it is not surprising that a regression model, relying on wind
speed, wind direction, and stability class data collected at just two
locations at a surface mine would have little success in predicting airflow
parameters in the pit. Atmospheric modeling is one of the most challenging
simulations performed today, and even very elaborate numerical models which
use complete descriptions of the upwind flow field (velocity, temperature,
diffusivity, vorticity), and exact representations of the terrain features,
have difficulty predicting characteristics of flow fields.
55
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5.4 COMPARISON OF ESCAPE FRACTION AND METEOROLOGY
Escape fractions were computed from four different methods in this study:
• SMOKE PUFF EXIT VELOCITY (SETTLING). Escape fractions were
determined for each of the roughly 800 smoke release episodes by
calculating the puff exit velocity for each episode, and then
entering the curves shown in Figures 4.3 and 4.4 to find the escape
fraction corresponding to the universal and the EDS particle size
distributions, respectively. This effort, which was computerized,
yielded roughly 1600 escape fractions (800 for the universal
distribution and 800 for the EDS distribution). These escape
fractions were written on magnetic tape for subsequent statistical
analysis.
• SOURCE DEPLETION (DEPOSITION). Escape fractions were determined for
each of the roughly 800 smoke release episodes by computing the
escape fraction directly from the familiar source depletion
equation. This effort, which was computerized, yielded roughly 1600
escape fractions (800 for the universal distribution and 800 for the
EDS distribution). These escape fractions were written on magnetic
tape for subsequent statistical analysis.
• FABRICK EQUATION. Values of wind speed, effective pit width, and
deposition (or settling) velocity were entered into the Fabrick
escape fraction equation (section 4.5.7) for each smoke release
episode and for both the universal and the EDS size distributions.
The individual values of escape fraction determined by the Fabrick
equation were written on magnetic tape for subsequent statistical
analysis.
• WINCES EQUATION. Values of pit depth, vertical diffusivity, and
deposition or settling velocity were entered into the Winges
equation (section 4.5.8) for each smoke release episode and for both
the universal and the EDS size distributions. The individual values
of escape fraction determined by the Winges equation were written on
magnetic tape for subsequent statistical analysis.
The values of escape fraction inferred from the smoke puff exit
velocity, inferred from the source depletion equation, calculated from the
Fabrick equation, and calculated from the Winges equation, are shown in Table
5.9, grouped by stability class.
56
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TABLF 5.9
ESCAPE FRACTION BY STABILITY CLASS
DISTRIBUTION
UNIVERSAL
EDS
P-G STABILITY
1
2
3
4
6
1
2
3
4
6
EXIT VELOCITY
(SETTLING)
1.00
1.00
1.00
1.00
1.00
0.81
0.85
0.93
0.90
0.70
SOURCE DEPLETION
(TEPOSITION)
0.93
0.88
0.86
0.81
0.58
0.59
0.46
0.43
0.36
0.21
WINCES
0.99
0.98
0.96
0.92
0.58
0.90
0.84
0.73
0.59
0.20
FABRICK
0.58
0.72
0.85
0.88
0.68
0.11
0.17
0.28
0.32
0.14
From Table 5.9, it is seen that escape fractions for the 0-30 micron particles
that make up the universal particle size distribution are larger than those
associated with the 0-130 micron EDS distribution. This is true for all four
escape fraction computation methods, and reflects the fact that deposition
velocity and settling velocity are parameters that appear in the computation
of all four escape fractions. Intuitively it seems reasonable that a greater
fraction of the large EDS distribution particles would be retained in the pit
than would the smaller diameter universal distribution particles.
The escape fractions inferred from field data by both the ex:t velocity
(settling) and the source depletion (deposition) methods clearly show that
less particulate escapes from the pit during stable ("F" class) conditions
than during other stability classes. This finding suggests that the very
stable atmosphere suppresses vertical motion, causing more particulate to be
retained in the pit. The Winges escape fractions agree well with the escape
fraction inferred from the source depletion calculations, and are also much
smaller during stable conditions than during unstable and neutral conditions,
which reflects the presence of vertical diffusivity (K ) in the Winges
equation. The Fabrick escape fractions do not exhibit the characteristic
decrease in magnitude with "F" stability. This is to be expected, since the
Fabrick escape fraction equation is not a function of stability class or
vertical diffusivity.
57
-------
An evaluation of the variations in escape fraction with wind speed
category, shown in Table 5.10, exhibits the same trend as the variation in
escape velocity discussed earlier. The magnitudes of escape fraction increase
with increasing wind speed, for all four escape fraction techniques. However,
TABLE 5.10
ESCAPE FRACTION BY WIND SPEED CLASS
DISTRIBUTION
UNIVERSAL
EDS
WS CLASS
1
2
3
4
c
1
2
3
4
5
EXIT VELOCITY
(SETTLING)
1.00
1.00
1.00
1.00
1.00
0.75
0.85
0.96
0.96
0.99
SOURCE DEPLETION
(DEPOSITION)
0.78
0.84
0.86
0.88
0.88
0.35
0.46
0.43
0.43
0.43
WINCES
0.90
0.91
0.95
0.95
0.96
0.70
0.70
0.73
0.69
0.76
FABRICK
0.60
0.81
0.87
0.92
0.93
0.10
0.20
0.28
0.38
0.43
escape fractions determined by the Fabrick equation exhibit slightly better
agreement with escape fractions inferred by the settling and deposition models
than do the escape fractions determined by Winges equation. The reason for
this may be that the Winges equation does not include wind speed as an
explicit parameter, as does the Fabrick equation. Inclusion of wind speed
directly into the Winges equation may improve its ability to match the trends
observed in the escape fractions inferred from the settling and deposition
models.
The differences between escape fractions inferred by the exit velocity
(settling) method and the source depletion (deposition) method raises an
obvious question: "Which method is correct?" Unfortunately, no answer is
possible because the field test was not designed to measure escape fraction-
directly, nor do the data lend themselves to straightforward computation of
escape fraction. Both the exit velocity and the source depletion methods used
to infer escape fractions incorporate numerous assumptions (see Sections 4.5.5
and 4.5.6 of this report), the validity of which cannot be checked with the
available data. However, it should be remembered that there is some reason to
58
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suspect that the escape fractions inferred from the smoke puff exit velocity
may be too large, especially for small diameter particles. The reasons for
this are that the computation of exit velocity for some of the smoke puff
episodes probably overestimates that velocity (see Sections 4.2 and 4.5.4),
and that the exit velocity method does not take into account the plume-terrain
interaction that governs particle deposition (see Section 4.5.5).
The grouping of escape fractions by mine is not very instructive. There
is no apparent trend or pattern discernible for any of the escape fractions.
-------
6.0 SUMMARY OF FINDINGS
Data from over 800 smoke release experiments were analysed to describe
the removal mechanisms and dispersion affecting particulate emissions
occurring within surface mine pits. An escape velocity, essentially the net
upward velocity within each pit, was computed from the observed retention time
of the tracers and the depth of each pit. This upward velocity, when compared
to the downward settling and deposition velocity for different size particles,
was the basis for the calculation of an escape fraction — the percentage of
particulate emissions expected to escape the mine pit. Independently, the
source depletion equation was used, in conjunction with wind measurements made
in the mine pits, to compute escape fraction. These computed escape fractions
were then compared to escape fractions computed using methodologies proposed
by Fabrick (1982) and Winges (1981). In addition, meteorological measurements
performed inside the mine pits were compared to simultaneous out of pit
measurements. Finally, observed movements of the smoke tracer plumes were
categorized and then compared to the meteorological conditions occurring at
the same time.
The following conclusions are presented from the findings of these
analyses:
• Computed escape velocities and escape fractions are lowest during
night-time, stable atmospheres, and during light wind speeds. This
finding is in agreement with observed flow patterns in the mine
pits, as the released smoke tracer was frequently observed to have
stagnated within the mine pits during these conditions. Conversely,
the greatest ventilation rates were observed during high wind speeds
and near neutral atmospheres.
• The computed escape velocity was found to be positively correlated
with measured wind speed, temperature, and wind direction, and
negatively correlated with stability category, and the width of the
mine pit. However, when these parameters were used in linear,
multivariate regression analysis, only 32% of the variation in
escape velocity values could be accounted for. The linear model
could not be improved upon through the use of in-pit measurements
rather than out-of-pit measurements, or by stratifying the data by
mine, by stability class, or wind speed category. It is concluded
that some processes or variables, not accounted for in this
analysis, must act in conjunction with the above meteorological
parameters to determine the escape velocity.
61 -
-------
In-pit winds are significantly different from out-of-pit winds. The
in-pit wind direction differs from the out-of-pit wind direction by
about 60°. Further, no correlation between the in-pit versus
out-of-pit wind direction was found using linear regression
techniques, hence the in-pit wind direction can not accurately be
predicted from a knowledge of the out-of-pit direction. In-pit wind
speeds are, on the average, 25% smaller than out-of-pit wind
speeds. Linear regression analysis did identify a significant
positive correlation between in-pit and out-of-pit wind speeds.
62
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7.0 RECOMMENDATIONS FOR FUTURE WORK
It appears that there are two data needs that must be addressed in the
future to gain a better understanding of the pit retention phenomenon. First,
there is clearly a need to quantify the magnitude of pit retention with direct
measurements of concentration, or particle flux, so that a data base suitable
for evaluating the performance of pit retention algorithms will be available.
This direct measurement would also provide an estimate of the range of pit
retention escape fractions, and should give EPA hard data with which to answer
policy questions:
- Does the magnitude of pit retention warrant corrections to existing
models?
Will pit retention affect PM-10 concentrations?
Is the fluctuation in escape fractions within the error band
("noise") of particulate emission factors?
At the same time, there is a need to continue to identify and quantify
the parameters that influence pit dispersion. Without an understanding of the
dispersion, transport, and removal mechanisms that affect surface mine pits,
there is little hope of simulating them.
With these needs in mind, the authors offer a series of recommendations
for future study:
• MODEL COMPARISON. A very simple and inexpensive investigation can be
performed to determine a "ballpark" magnitude of pit retention. Using
existing hi-vol data and meteorological data already collected in the vicinity
of surface mines, a comparison can be made of actual measured concentrations
just downwind of a pit (cmeasured), and modeled concentrations determined
from the ISCST model (cmodeled)> which idealizes the terrain as flat and
unaffected by the presence of the pit. Emission rates would be estimated from
AP-42, Supplement 14 fugitive dust factors, and a representative background
concentration (perhaps from an upwind hi-vol) would be subtracted from the
63
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measured concentrations. Any departure in the value of (C ,/
measured
C , , ,) from 1.0 would be due to errors in the emission factors, or to
errors in the model. If a long time period is considered — perhaps by
examining annual average concentrations — then random errors in the model and
emission factors will cancel out. Differences in the value of (C /
measured
C , , ,) from unity would be due to systematic errors, such as pit
retention or plume perturbation caused by the pit. In the absence of
systematic errors in the emission factors or in idealizing the dust plume, the
ratio of (cmeasured/C modeled^ would be Just equal to the escape fraction
for the particle size distribution collected by the hi-vols. This approach
would be a "first-cut" at estimating the magnitude of pit retention. Of
course, it would offer no insight into the physical mechanisms that control
dispersion from the pit. A study of this sort, using existing data, would
cost from £10,000 to $20,000.
o PARTICULATE MEASUREMENT PROGEAM. A logical extension of the MODFL
COMPARISON investigation just described would be to measure particulate
concentrations at the downwind edge of a mine pit, while measurements of
meteorological variables are being made. As before, the ratio of measured and
modeled concentrations would provide a measure of pit retention. However, the
availability of detailed meteorological data, especially stability class, wil]
allow an examination of relationships between pit escape fraction and
meteorology.
The length of each test, or episode, must be at least 15 minutes, which
is the minimum time needed to approach Guassian distributions and therefore
derive pit retention algorithms for the existing Gaussian models. The maximum
time duration for each test will be dictated by how much particulate matter
must be collected by the samplers to provide reliable concentrations. Use of
filter samplers suspended from a tethered balloon, and use of a quartz crystal
microbalance, as suggested by Air Sciences (Hittman and Air Sciences, 1983),
should be considered. The cost for these tests would be between $5100,000 and
$150,000.
64
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• PARTICULATE TRACER RELEASE. Both of the studies suggested above are
unable to differentiate between systematic errors in emission factors and the
effect of pit retention. If, for example, the ratio of cn,easured/CII,odeled
is found to be 0.80, it is not certain whether the escape fraction of
particulate is .80, or whether the emission factors are consistently low by 20
percent. This problem can be overcome by controlling the emission rate by
using a tracer.
The portion of the mine dust particulate matter that is retained in the
pit can be determined by employing a particulate tracer material that matches
the settling velocities of the mine particulate. This tracer material would
be emitted continuously for a short time, on the order of an hour, at a
controlled rate from a simulated source in the pit. Measurements of the
particle budget outside the pit would in turn establish the fraction that
escaped from the pit to the ambient atmosphere. The difference between the
escaped fraction and the emitted material would then represent the portion
retained, or the pit retention fraction.
The mass flux of particulate tracer material downwind of the pit would
be determined by measuring the concentration field. The methodology to
measure this field of concentrations includes the use of several towers tall
enough to encompass the vertical extent of the tracer plume, a broad enough
array of towers to encompass the plume laterally, and with enough measurement
points to enable rigorous reconstruction of vertical profiles of the tracer
plume. With these concentration measurement points in a crosswind vertical
array a specific cross sectional area is represented by each measurement
point. A budget of particles observed can then be determined from a cross
wind vertical integration of all these cross sectional areas.
Requirements:
• Tracer material in size range to match dust particulate settling
rate.
• Assay of bulk tracer material to determine mean mass diameter and
particle size distribution by percent of particles in size
categories.
• Assay of observed concentration times with particle size
distribution by percent in particle size categories at each sampler
location.
65
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FIGURE 7.1
PARTICLE LIDAR EXPERIMENT
Ambient
Wind
66
-------
The following observations and measurements would be made:
PHYSICAL FACTORS
DETERMINE
Pit parameters
Tracer Source
location
MEASURE
Length
Width
Depth
Orientation
Location
Dissemination rate
ENVIRONMENTAL FACTORS
DETERMINE
Atmospheric Stability
out of pit
Average wind speed
Stability out of pit
Stability in pit
Vertical profile of
Wind speed
Concentration field
(tracer)
MEASURE
Cloud cover
Time of day
Wind speed
(out of pit)
Statistics of
wind speed
variability
Wind direction
(out of pit)
Statistics of
wind direction
variability
Wind speed
(in pit)
Statistics of
wind speed
variability
Wind direction
(in pit)
Statistics of
wind direction
variability
Wind speed at
several height
on one tower
Concentration
at several
heights on each
tower
Time on-off
each sampler
The tracer material to be employed is a broad-band particle size
fluorescent particle material. This material can readily be distinguished
from dust when assayed under ultraviolet radiation. The bulk fluorescent
particle material (FP) would have to be obtained in a size range and width of
size ranges to duplicate a typical range of dust settling rates. That is,_
since the FP material is normally of specific density in a different range
67
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than dust, it would be necessary to match the dust and FP by equivalent
settling rates. Dust from mines can be expected to be in the specific density
range near 2.0 while the most commonly used FP is specific density 4.0. If
the dust size range can be expected to vary from 2 to 100 microns then FP in
the range from 1 to 50 microns could be employed so long as the particle shape
factor is roughly equivalent. There is however, some anticipation that FP
material cannot be obtained in a size range extending as large as 50 microns.
A size range up to 20 urn is more likely. Alternately fluorescent dyed glass
beads of sizes 50 to 100 microns with a specific density of 2.0 could be
combined with the FP to fill out the full size range of concern.
The specific information that would be derived from such an experiment
would include the following:
• Escape fraction and retained fraction in each particle size category
by in-pit and out-of-pit stability class, wind speed class, pit
parameter.
• Lateral and vertical plume dimension at the location of exit from
pit, categorized by in-pit and out-of-pit stability class, wind
speed class, and pit parameters.
The relative cost of performing particulate tracer experiments would be
considerably higher than the previous experiment discussed. Particulate
tracer work is quite labor intensive, in addition to the field measurement
area. Due to the high cost of the manual optical assay techniques the overall
cost of such a program probably would exceed £300,000.
• PARTICULATE FLUX MEASUREMENT
The final level of complexity and rigor suggested here involves making
Lidar measurements of the actual dust source in the pit and companion I.idar
measurements of the dust plume shortly after it has exited from the pit.
Several types of Lidar devices are sensitive to dust particles. That is, they
can detect the presence of dust particles and in turn can determine relative"
concentrations of dust particles. The CO- Lidar is probably the most
sensitive to dust and has the added advantage of being "Eye Safe" beyond a few
feet range. The Lidar devices have been developed to the point they are range
68
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gated and thus can give mean concentrations for increments of range. This
means that one can obtain an along-path concentration profile for each firing
of the Lidar beam. The range gating yields a concentration profile, along
path, integrated based on mean concentrations for every 3 meters of Lidar beam
range.
The approach to be employed with this device would be to locate one
Lidar in the pit to measure a crosswind vertical profile of the concentration
of dust generated by the activities of the mining operation. A second Lidar
would be located to the side of the pit so it could do a cross wind profile
just downwind of the pit. This second Lidar would be located so as to scan
directly cross wind at a constant azimuth angle starting with a horizontal
position and make successive firings at successively increased elevation
angles. This would yield a crosswind vertical profile of dust concentration.
Concurrent measurements of wind profiles both in-pit and out-of-pit would be
needed to enable calculations of out-of-pit dust particle budgets which would
be compared to the in-pit budgets to determine pit retention. A schematic of
the experimental configuration is shown in Figure 7.1
69
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The following observations and measurements would be made:
PHYSICAL FACTORS
DETERMINE
Pit parameters
Dust Sources
MEASURE
Length
Width
Depth
Orientation
Locations
Activity level
ENVIRONMENTAL FACTORS
DETERMINE
Atmospheric Stability
out of pit
Average wind speed
Stability out of pit
Stability in pit
Vertical profile of
Wind speed (out of
pit)
Dust profile (in pit)
Dust profile (out of
pit)
MEASURE
Cloud Cover
Time of day
Wind speed
(out of pit)
Statistics of
wind speed
variability
Wind direction
(out of pit)
Statistics of
wind direction
variability
Wind speed
(in pit)
Statistics of
wind speed
variability
Wind direction
(in pit)
Statistics of
wind direction
variability
Wind speed at
several height
on one tower
Lidar profile
Lidar profile
The cost of conducting a Lidar field measurement program would be relatively
high, most probably on the same order as the tracer experiment, that is, about
£300,000.
The advantage in employing Lidar measurements is that they would provide
a direct measurement of the dust behavior itself, rather than a measurement of
a simulant as a tracer study would. The disadvantage is that there would be
no information generated about particle size distributions unless extra
observations were incorporated for that specific purpose.
70
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• FEDEEAL HIGHWAY ADMINISTEATION STUDY
As explained in Section 2.0, BACKGROUND AND LITERATURE SURVEY, the
Federal Highway Administration will fund a 20 month long wind tunnel study of
airflow and dispersion in street canyons and deep cuts. One purpose of the
study will be to develop algorithms that can be used to improve predictions of
CO concentrations in existing Gaussian models. EPA may choose to monitor this
study since it will likely offer some insight into mine pit flows. Or, EPA
may even consider funding an expansion of the FHWA study to include
investigation of surface mine pits.
71
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REFERENCES
AMS, 1977, "AMS Workshop on Stability Classification Schemes and Sigma Curves
— Summary of Recommendations", appearing in Bulletin American
Meteorological Society, vol 58, no. 12, December 1977, pp.1305-1309
Bowne, N.E., et al, 1S82, "Overview, Results, and Conclusions for the EPRI
Plume Model Validation Project: Plains Site", for Electric Power
Research Institute, Palo Alto, CA., Project 1616, November 1982.
Cole, C.F., and A.J. Fabrick, 1984 "Surface Mine Pit Retention", Journal Air
Pollution Control Association, vol 34, no. 6, June 1984, pp. 674-675.
Cole, C.F., 1984, personal conversation with Rodger Steen, Air Sciences, Inc.,
July 20, 1984.
Draxler, R.F., 1977, "A Mesoscale Transport and Diffusion Model", NCAA Tech.
Memo, ERL-ARL-64, p. 31.
EPA, 1980, "Workbook for Estimating Visibility Impairment", OAQPS, contract
68-02-3337, July 1980.
Fabrick, A.J., 1982, "Technical Note: Calculation of the Effective Emissions
from Mine Pit Operations by Incorporating Particulate Deposition in the
Excavated Pit", MEF Environmental, Del Mar, CA, 1982.
Gresho, P.M., et al, 1976, "Modeling the Plantetary Boundary Layer Using the
Galerkin Finite Element Model", presented at Third Symposium on
Atmospheric Turbulence, Diffusion, and Air Quality, Raleigh, NC, Oct.
26-29, 1976.
Hanna, S.R., G.A. Briggs, and R.P. Hosker, 1982, Handbook of Atmospheric
Diffusion, DOE/TIC-11223, Technical Information Center.
Herwehe, J.A., 1984, "Numerical Modeling of Turbulent Diffusion of Fugitive
Dust from an Idealized Open Pit Mine", masters thesis, Iowa State
University, Ames, Iowa. 1984.
Herwehe, J.A., 1984a, private communication x/ith Clifford Cole, July 20, 1984.
Hittman and Air Sciences, 1983, "Studies Related to Retention of Airborne
Particulates in Coal Mine Pits—Data Collection Phase", prepared for
U.S. EPA, IEFL, Cincinnati, Ohio, contract #68-03-3037, August 1983.
Hogstrom, A.S., and U. Hogstrom, 1978, "A Practical Method for Determining
Wind Frequency Distributions for the Lowest 200 m. from Routine
Meteorological Data", Journal Applied Meteorology, vol 17, July 1978,
pp. 942-953.
Irwin, oT.S., 1980, "Dispersion Estimate Suggestion #8", metro from J.S. Irwin
to Regional Meteorologists, July 31, 1980.
73
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Lapple, C.E. 1961, "Characteristics of Particles and Particle Dispersoids",
laboratory wall chart reprinted from Stanford Fesearch Institute
Journal, Third Quarter, 1961, SPI International, Menlo Park, CA.
Lavery, T.F., et al, 1982, "EPA Complex Terrain Model Development First Mile-
stone Report", for Environmental Protection Agency, Environmental
Research Laboratory, Fesearch Triangle Park, NC, PB82-231713, April 1982.
Martin, D.O., 1976, "Comment on the Change of Concentration Standard
Deviations with Distance," Journal Air Pollution Control Association,
vol. 26, No. 2 (February 1976), pp. 145-146.
Mitchell, A.E., and K.O. Timbre, 1979, "Atmospheric Stability Class from
Horizontal Wind Fluctuation," presented at 72nd meeting APCA,
Cincinnati, Ohio, June 24-29, paper no. 79-29.2, pp. 16.
Nelli, J.P., et al 1983, "Analysis and Modeling of Air Quality at Street
Intersections," Journal Air Pollution Control Association, vol 33, no.
B, August 1983, pp. 760-764.
Pasquill, F., 1974, Atmospheric Diffusion, 2nd ed., John Wiley and Sons, New
York.
PEDCo & TRC, 1982, "Characterization of PM-10 and TSP Air Quality Around
Western Surface Coal Mines," prepared for EPA, Air Management Technology
Branch, contract #68-02-3512, June 1982.
Shearer, D.L., et al, 1981, "Coal Mining Emission Factor Development Study,"
prepared by TEC Environmental Consultants, Inc., 0908-D10-15, Englewood,
CO July 1981.
Turner, D.B., 1970, "Workbook of Atmospheric Dispersion Estimates," EPA,
OAQPS, AP-26, 1970.
Van der Hoven, I., 1968, "Deposition of Particles and Gases," appearing in
Meteorology and Atomic Energy 1968, ed. D.H. Slade, Technical
Information Center, U.S. DCE, TID-24190, July 1968.
Wedding, J. B., et al, 1977, "A Wind Tunnel Study of Gaseous Pollutants in
City Street Canyons," Journal Air Pollution Control Association, vol.
27, p. 557.
Winges, K.D., 1981, "Description of the EFTEC Mining Air Quality Model," ERTEC
Northwest, Inc., Seattle, WA 1981.
Zamars, J., and R. Piracci, 1982, "Modeling of Carbon Monoxide Hot Spots,"
Journal Air Pollution Control Association, vol. 32, no. 9, Sept. 1982,
pp. 947-953.
74
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APPENDIX 1
-------
BIBLIOGPAPHY
Chan, S.T., "Numerical Simulations of Atmospheric Releases of Heavy Gases over
Variable Terrain", Lawrence Livermore Laboratory, UCPL-87256, August
1982.
Chan, S.T., "FEM3—A Finite Element Model for the Simulation of Heavy Gas Dis-
persion and Incompressible Flow User's Manual", Lawrence Livermore
National Laboratory, UCPL-53397, February 1983.
Cole, C.F., and A.J. Fabrick, "Surface Mine Pit Petention", Journal Air
Pollution Control Association, vol 34, no. 6, June 1984s pp. 674-675.
Fabrick, A.J., "Technical Note: Calculation of the Effective Emissions from
Mine Pit Operations by Incorporating Particulate reposition in the
Excavated Pit", MEF Environmental, Del Mar, CA, 1982.
Gresho, P.M., et al, "Modeling the Plantetary Boundary Layer Using the Galerkin
Finite Element Model", presented at Third Symposium on Atmospheric
Turbulence, Diffusion, and Air Quality, Raleigh, NC, Oct. 26-29, 1976.
Herwehe, J.A., "Numerical Modeling of Turbulent Diffusion of Fugitive Dust
from an Idealized Open Pit Mine", masters thesis, Iowa State University,
Ames, Iowa. 1984.
Hoydysh W. G. and Ogawa "Characteristics of Wind Turbulence and Pollutant
Concentration in and Above a Model City." Report No. EEPL NYU 110,
Environmental Engineering Research Laboratories, New York University,
Bronx, New York.
Hoydysh W.G. and Ogawa "A Two-Dimensional Study of Dispersion of Automotive
Pollution in Street Canyons", Report No. EEPL NYU 111, Environmental
Engineering Research Laboratories, New York University, Bronx, New York.
Johnson, W.B., et al, "Field Study for Initial Evaluation of an Urban
Diffusion Model for Carbon Monoxide", Stanford Research Institute, SPI
Project 8563, June 1971.
Lee, R.L., et al, "A Modified Finite Element Model For Application to Terrain-
Induced Mesoscale Flows", Lawrence Livermore National Laboratory,
UCRL-88033, November 1982.
Lee, R.L. and J.M. Leone, Jr., "Numerical Calculations of Stratified Ekman
Layer Flow Over Ridges with a Finite Element Model", Lawrence Livermore
National Laboratory, UCRL-90668, June 1984.
Ludwig, F.L., and W.F. Dabberdt, "Evaluation of the APPAC-1A Urban Diffusion'
Model for Carbon Monoxide", SRI Project 8563, February 1972.
Mehta, U.B., and Z. Lavan, "Flow in a Two-Dimensional Channel with a Rectangu-
lar Cavity", Transactions of the ASME, Dec. 1969, pp.897-901.
A-l
-------
Nelli, J.P., et al, "Analysis and Modeling of Air Quality at Street Inter-
sections", Journal Air Pollution Control Association, vol 2?, no. 8,
August 1983, pp. 760-764.
Pan, F., and Acrivos, A., "Steady Flows in Rectangular Cavities", Journal
of Fluid Mechanics, Vol. 28, 1967, pp. 643-655.
Wedding, J.B., et al "A Wind-Tunnel Study of Gasecus Pollutants in City Street
Canyons", Journal Air Pollution Control Association, vol. 27, p. 557.
Winges, K.D., "Description of the ERTEC Mining Air Quality Model", EETEC
Northwest, Inc., Seattle, WA, 1981.
Zarcars, J., and R. Piracci, "Modeling of Carbon Monoxide Hot Spots", Journal
Air Pollution Control Association, vol 32, no. 9, September 1982, pp.
947-953.
A-2
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TECHNICAL REPORT DATA
(Please read Instructions on the reverse before completing)
1. REPORT NO. 2.
EPA-450/4-85-001
4. TITLE AND SUBTITLE
Dispersion of Airborne Particulates In Surface Coal
Mines, Data Analysis
7. AUTHOR(S)
9. PERFORMING ORGANIZATION NAME AND ADDRESS
TRC Environmental Consultants, Inc.
7002 South Revere Parkway, Suite 6
Englewood, Colorado 80112
12. SPONSORING AGENCY NAME AND ADDRESS
Monitoring and Data Analysis Division
Office of Air Quality Planning and Standards
U. S. Environmental Protection Agency
Research Triangle Park, NC 27711
3. RECIPIENT'S ACCESSION NO.
5. REPORT DATE
January 1985
6. PERFORMING ORGANIZATION CODE
8. PERFORMING ORGANIZATION REPORT l>
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
68-02-3514
13. TYPE OF REPORT AND PERIOD COVERE
14. SPONSORING AGENCY CODE
EPA/200/04
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This report summarizes the results of an effort to better understand the
dispersion and transport of particulate matter released within surface coal mine pits
Data previously collected at four surface coal mines were used in this investigation.
This report describes the analysis and interpretation of those data, examines the
relationship between meteorology and smoke puff behavior, and compares mine pit escap
fraction (that portion of the dust emitted in the pit that leaves the pit) with those
predicted by existing equations.
Two independent techniques were used in conjunction with assumed particle size
distributions and the onsite data, to infer values of escape fraction. These values
were then used to determine the predictive ability of two widely used model algorithm
The report contains numerous tabulations and discusses the relative merits of each
method.
17. KEY WORDS AND DOCUMENT ANALYSIS
a. DESCRIPTORS
Air Pollution
Coal Mining Emissions
Particulates - Escape Fraction
Meteorology
18 DISTRIBUTION STATEMENT
Unlimited
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81
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