EFFECTS OF LOW QUARTZ MASS LOADING

appear to be outliers in Figure 5, it is entirely possible that these
high values of CVouside Could be due to exceptionally windy
conditions on those sampling days.

CONCLUSIONS

The results of this study show that, for samples collected
within the environment of an enclosed cab, 1) the P7 method
may be precise and linear with quartz mass loadings as low as
approximately 5 jug of quartz, 2) the environmental dust mass
spatial variability, even within an enclosed cab environment,
is the dominant factor of variability in propagation of error in
estimating the percentage of quartz of the obtained dust sam-
ples for weighing precision values up to at least 0.02 mg. In
addition, samples collected at various locations exterior to the
drill cab showed increased data scatter in the measured CVs at
these outside locations in comparison to the inside cab samples.
This strongly suggests that the ambient dust mass spatial vari-
ability, likely due to wind velocity and orientation effects, has
a significant effect on the propagation of error in estimating the
percentage of quartz of the outside dust samples.

REFERENCES
1. Ng, TP; Yeung, K.H.; O'Kelly, FJ.: Silica Hazard of Cais-
son Construction in Hong Kong. J Soc Occup Med 37:62-65
(1987).
. Ezenwa, A.O.: Studies of Environmental and Host Factors Influ-
encing Personal Differences in Response to Industrial Silica Dust
Exposure, Ann Occup Hyg 26(1-4):745~752 (1982).
Sacharov, K.M.; Knauss, K.G,; Kubala, PJ.: Reductions of Dust
Exposures in the Slate Industry. Am Indus Hyg Assoc J 32(1):119—
122 (1971).

v

»

°

oy

1.
12.

13,

. Tomb, T.F,; Peluso, R.G.; Parobeck, P.,

. Cecala, A]

923

. Burns, C.; Ottoboni, F.; Mitchell, H.W.: Health Hazards and Heavy

Construction. Am Indus Hyg Assoc J 23(1):273-281 (1962).
juartz in United States
Coal Mines. Ann Am Conf Gov Ind Hyg 14:513-519 (1986).
Cherniak, M.: Hawk’s Nest Incident: America’s Worst Industrial
Disaster, Hartford, CT: Yale Unive Press (1989).

U.S. Congress: Federal Coal Mine Health and Safety Act of 1969.
Public Law 91-1173 (1969).

. National Institute for Occupational Safety and Health: Collabora-

tive Tests of Two Methods for Determining Free Silica in Airborne
Dust. DHHS (NIOSH) Pub. No. 83-124. NIOSH, Cincinnati, OH
(1983).

). National Institute for Occupational Safety and Health: NIOSH

Manual of Analytical Methods, Fourth Ed. NIOSH, Cincinnati,
OH (1994).

). U.S. Code of Federal Regulations, Title 30 (Mineral Resources)

Part 74.3, Para, 11.

Squires, G.L.: Practical Physics. McGraw-Hill, London (1968).
Kennedy, E.R.; Fischbach, T.J.; et al.: Guidelines for Air Sam-
pling and Analytical Method Development and Evaluation. DHHS
(NIOSH) Pub. No. 95-117. NIOSH, Cincinnati, OH (1995).
Bartley, D.L.; Chen, C.C.; Song, R.; Fischback, T.J.: Respirable
Aerosol Sampler Performance Testing. Am Indus Hyg J 55:1036—
1046 (1994).

}. Bowman, J.D.; Bartley, D.L.; Breuer, G.M.; et al.: Accuracy Crite-

tia Recommended for the Certification of Gravimetric Coal Mine
Dust Samplers. NTIS Pub. No. 85-222446. NIOSH, Cincinnati,
OH (1984).

folkwein, J.C.; Timko, R.J.; Williams, K.L.: Veloc-
ity and Orientation Effects on the 10-mm Dort-Oliver Cyclone.
BuMines RI 8764 (1983).

Organiscak, J.A.; Page, S.J.: Assessment of Airborne Dust Gener-
ated From Small Truck-Mounted Rock Drills. BuMines RI 9616
(1996).


914

standard deviations. As will be seen in the Results section, the
pump error can legitimately be ignored. This is also a required
condition to allow using the RMS of the individual standard
deviations because the units of pump error are volumetric flow
and not mass. Although the standard deviation RMS equations
presented in the Results section appear to represent additive er-
ror propagation, they in fact remain as describing multiplicative
error propagation.

This analysis for CVcap remains conservatively low in that
any additional measurements producing a CVca) beyond the es-
timated upper confidence limit will show the nonlinearity in the
CVcap VS dust mass curve occurring at a larger dust mass value.
Also, any exceptions to assumptions (i)-(vi) will result in CV
terms of higher order which are not accounted for in Eq. 3. For
example, inter-variable dependency results in non-orthogonality
of the error vectors. This produces higher-order terms deter-
mined by the degree of inter-dependency, An alternative view of
the effect of inter-variable dependency is that non-independent
and completely correlated errors are combined by algebraic ad-
dition and not RMS addition, which has the effect of generating
a larger total error. Since the variables in this study are likely to
have low partial correlation, the effect of higher-order terms is
likely to be minimal.

RESULTS

The precision for a single weighing is estimated from the
control filters to be no more than 0.020 mg standard deviation.
Since sample filters were batch-weighed in short time frames,
Jong-term variations in weigh room humidity and temperature
would likely be minimized, producing a standard deviation lower
than 0.020 mg. The maximum standard deviation in net filter
weight gain, determined by pre- and post-weighing, is estimated
by the formula for additive propagation of errors to be

Sweighing = ((0.020)* + (0.020)°)}'? = .028 mg [6]

Since the sampler pump pooled CVpump was 0.5 percent for
initial calibration and assuming that flow control was adequately
maintained by the pump during the sampling period, it can rea-
sonably be considered as insignificant and will be ignored.

Based on the replicate analyses of each sample analyzed
for quartz, the single-sample P7 analysis had a pooled CVp7
of 2.2 percent and the composite samples had a pooled CVp;
of 2.0 percent on a quartz mass basis. The more conserva-
tive value of 2.2 percent was chosen for the regression of

Eq. 5.

Procedure A

Sixty valid (e., no known sampling malfunctions) single
and 55 valid composite quartz samples were obtained during
the study and are summarized in Table I. The data do not sug-
gest any benefit to classifying the data according to the particu-
lar piece of equipment being used. Therefore, analysis is based

S.J. PAGE ET AL.

upon the total number of samples obtained. The smaller number
of composite samples reflects the fact that, in some cases, the
individual filter weight gains were large enough that too much
mass would be present on the composite sample of three filters.
This weight gain is especially critical for surface mine samples,
where most of the dust mass on the filter is non-combustible
mineral matter. The average mass variability of the sampler
packages, expressed by pooling the values of CVpackages WaS
12.3 percent for the drill samples and 39.3 percent for the dozer
samples.

Figures 1 and 2 show the results of a comparison between the
single and composite samples in terms of micrograms of quartz
as determined by P7. The composite values represent the average
of three filters. It is apparent that the P7 method is precise and
linear with quartz mass loadings as low as approximately 5 ug
of quartz in field samples.

Figure 3 plots the error propagation, represented by CVcab,
present in the quartz percentage calculation as a function of the
sample mass. In Figure 3, both the positive and negative val-
ues Of CVcap obtained from sampler #2 are shown. (As a matter
of note, negative CV’s are not typically mentioned but they are
entirely valid mathematically since the equation for CVcap in-
volves a square root. Additionally, from the definition of CV,
‘one can have both (+) and (—) standard deviations, since av-
erage values are typically expressed as “mean value” + “stan-
dard deviation.”) The heteroskedastic nature of the sampling is
clearly evident, showing that CVcap is not constant across the
mass range below 1.6 mg of dust. The data scatter below 0.3 mg
is classic for multiplicative error propagation, illustrating that
errors can assume any value within an absolute boundary
which is dependent on all components of variation. Regression
of Eq. 5 on the outermost points of the data set (identified in
Table I) produced a value of Stimit = 072 mg. The validity of
representing Siimix a8 a regression constant is established by the
fact that for the data in Table I the standard deviations Spackage
of the sampler packages are quite linear and highly uncorre-
lated with the average package dust mass across the mass range
0.03 mg to 1.6 mg, as shown in Figure 4. Regression of Spactage
against the average mass (one obvious outlier excluded) pro-
duced the equation y = 0.016x + 0.021, R? = 0.15, showing
only a 1.6 percent change in Spackage Over a five-fold change in
dust mass, When x = 0 (no filter mass collected), the predicted
standard deviation is 0.021 mg. This corresponds to the condi-
tion when no sample is taken and the only error present is the
weighing error, measured to be 0.020 mg for a single weighing
since there is no need for two weighings. Thus, this is further
supportive evidence that established the validity of the measured
error in a single weighing as well as assuming the quantity of
data to be sufficient for interpreting Sjimie to represent a suitable
upper confidence limit.

Using Sweighing from Eq; 6 and the formula for multiplicative
error propagation valid for all mass values, the remaining vari-
ability consisting of cyclone and dust mass spatial variability
within the cabs can be estimated. This remaining variability is


EFFECTS OF LOW QUARTZ MASS LOADING

919

CAB SAMPLES, DRILLS AND DOZERS

¢ data limit
$ 0.020 mg stdev weight
100% + x .001 mg stdev weight
3
3/8
50% © 52.4%
0 35.3%
, bag!
Rog
os *s, oh 8 Fuge as
0 0.2 04 06 08 1 41.2 14 16 1.8
SINGLE FILTER MASS, mg
FIGURE 5

Maximum coefficient of variation (CV) versus single sample respirable dust mass, all sampler packages for drill and dozer cabs.
Regression values shown, represented by open squares and Xs, indicate sensitivity analysis for weighing precision.

for quartz, each filter mass gain from each sampler package could
be considered as a measurement of the airborne dust and was
compared to the CVoutside for that package. Therefore, the other
filter mass gains (from samplers 1, 3, and 4) in each package
are entered as data points in Figure 6. Figure 6 shows values
Of CVouiside aS large as 50 percent were obtained for sample
dust masses up to nearly 3.5 mg. It appears that, with respect
to the NIOSH Accuracy Criterion, much less certainty can be
placed on quartz percentage determinations based on outside
samples when dust mass spatial variability is included in the
analysis.

TABLE II
Values of S resulting from single weighing S values
of .020 and .001 mg

S,mg
Single weight 0.020
Sweighing 0.0283
Stota 0.0723
Sspait +-cycione (by difference) 0.0665
Single weight 0.0010
Sweighing 0.0014
Sspatial + cyclone (by difference) 0.0665
Stout 0.0665

DISCUSSION

Procedure A represents what is perhaps a dust sampling con-
dition that is as near to ideal as can be expected in a mining
environment. Within the cab environment, air currents are low
velocity (compared with the outside ambient conditions). How-
ever, dust mass spatial variability can be expected due to air
movement created by the cab air system and movement of the
operator himself.

In comparison to the results of procedure A, the increased
data scatter obtained in procedure B strongly suggests that the
ambient environment variability has a significant effect on the
sampler package CVs. The effects of wind velocity and orienta-
tion on cyclone performance have previously been documented.
Cecala et al.) concluded that, depending on cyclone orien-
tation, the cyclone could either over- or under-sample by ap-
proximately 40 percent at 2000 ft/min (23 mph) ambient air
velocities. Clearly, wind speeds on a surface coal mine bench
can gust to this value, if not exceed it. Organiscak et al. ob-
served that cyclone pairs oriented with inlets pointed into the
wind (parallel) averaged 14.07 mg/m®, and 9.38 mg/m? with in-
lets perpendicular to the wind. The average CVs for parallel and
perpendicular orientation pairs were 20 and 15 percent, respec-
tively. It is reasonable to suggest that outside samples at surface
coal mines under these conditions are susceptible to significant
airborne dust mass spatial variability. Although there were no
known sampling malfunctions for the data above 1.00 mg that


916 S.J. PAGE ET AL.

TABLET
Summary of in-cab sample quartz analysis and resultant coefficients of variation (CVs)
for percentage of quartz (single sample) calculation ( (Continued)

Single Combined Data Regression*
Wr aeane Net dust
_Average of 3 scans weight, CVpatage TOV OVE 4.CVey —_ +CVeen
pg quartz jg quartz mg pet pet 0.02 mg® 0.001 mg®
120.2 125.2 0.441 8.0 8.0 -8.0 16.5 15.2
97.6 106.0 0.444 3.6 3.8 -3.8
88.3 85.6 0.448 21 24 24
134.2 143.7 0.458 6.7 68 6.8 15.9 14.7
235.0 227.2 0.499 43 48 48 14.7 13.5
147.4 150.1 0.636 47 48 11.6 10.7
152.7 153.3 0.725 43 47 10.2 94
239.4 0.834. 46 46 8.9 8.3
196.1 0.838 31 3.2 8.9 8.2
343.4 330.7 1.037 5.0 54 73 68
278.8 288.3 1,128 40 47 6.8 63
492.7 1.219 2.5 2.7 63 59
520.3 1.272 3.0 3.1 6.1 5.7
712.5 665.9 1.456 44 45 54 5.1
591.9 1,542 3.2 3.5 5.2 48

Regression values at points which represent the outermost boundary of the error propagation.
Regression values corresponding to a net weight precision of 0.02 mg.
Regression values corresponding to a net weight precision of 0.001 mg.

CAB SAMPLES, DRILLS AND DOZERS

400

© y = 0.95x + 3.59
= 300 R?= 1.00
3

3

32

2 8 200
E

<

2

fe]

t')
0 50 100 160 200 250 300 350 400
QUARTZ, 1g, single sample
FIGURE 1

P7 quartz analysis comparison of a single sample versus a composite of three remaining samples in the sampling package,
0 to 350 jag quartz.


EFFECTS OF LOW QUARTZ MASS LOADING 915

TABLEI
Summary of in-cab sample quartz analysis and resultant coefficients of variation (CVs)
for percentage of quartz (single sample)-calculation

Single | Combined Data Regression
woe ene CNet dust
_Average of 3 scans eight, — CVpactage TOV Ve 4.CVeay + CVeab
peg quartz jug quartz mg pet pet 0.02 mg? 0.001 mg
3.8 4.0 0.031 13.4 13.5 -13.5
21.3 21.9 0.038 29.9 30.0 —30.0
9.2 9.5 0.064 50.5 50.6 —S0.6
5.6 8.7 0.068 9.1 9.5 —9.5
18.2 19.1 0.069 109.6 109.6 —109.6 104.8 96.5
12.9 10.8 0.084 24.9 25.0 —25.0
Vd 12.6 0.084 26.0 27.3 27.3
27.2 27.1 0.089 12.8 12.9 -129
43.8 41.0 0.100 8.1 8.3 8.3
0.0 19 0.100 68.7 68.7 —68.7 72.3 66.6
13.1 12.2 0.102 15.8 15.8 -15.8
36.2 34:5 0.111 22.2 22.4 22.4
30.5 33.7 0.114 12.0 12.0 —12.0
46.1 58.1 0.114 11.3 11.3 —11.3
56.1 54.2 0.118 22.9 23.0 —23.0
12.7 12.5 0.119 37.6 37.7 37.7
13.0 12.6 0.121 12.1 12.2 12.2
46.5 44.4 0.123 21.6 21.6 —21.6
29.0 29.0 0.125 8.9 8.9 8.9
13.9 14.3 0.125 10.1 10.5 —10.5
20.3 15.8 0.136 29.7 29.8 —29.8
42.1 46.1 0.137 18.1 18.1 —18.1
55.2 50.2 0.142 38.5 38.5 —38.5
73.3 75.7 0.147 6.2 6.5 —6.5
52.1 50.8 0.149 14.5 14.5 -14.5
17.0 16.7 0.161 14.3 14.5 -14.5
23.0 22.7 0.164 12.4 12.5 125
44.0 44.9 0.180 26.8 27.0 —27.0
4.0 47 0.183 30.8 31.0 —31.0
13:2 14.8 0.185 52.3 52.4 52.4 39.1 36.0
39.9 40.5 0.185 6.5 6.6 6.6
92.2 95.2 0.192 11.6 11.7 -11.7
51.6 46.1 0.197 6.6 6.6 —6.6
28.1 31.1 0.217 75 8.7 8.7
73.2 76.1 0.225 22.2 22.2 —22.2
51.6 514 0.239 75 719 -719
120.6 109.9 0.247 4.6 47 47 .
46.2 51.1 0.278 35.3 35.3 35.3 26.1 24.0
23.3 25.8 0.282 17 1.8 —18
15.8 22.5 0.315 9.4 10.2 -10.2
50.2 50.1 0.381 63 6.5. -6.5 o
159.8 167.7 0.390 2.8 3.1 -3.1 o
19.4 20.5 0.396 9.5 10.7 —10.7 18.4 16.9 ,
160.6 161.4 0.407 9.4 9.7 9.7 179°, 16.5 ‘
104.3 107.5 0.432 0.8 11 -hl :

(Continued on next page)


918

S.J. PAGE ET AL.

CAB SAMPLES, DRILLS AND DOZERS

0.25
£ o20 | y = 0.016x + 0.021
= 0. 9
z =U.
z R? = 0.152
-
< 0154
a
a
a
Zz 0.10 4
<
a
Z
SS 0.05 +
n
0.00 T T T T T r r
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
AVERAGE MASS, mg
FIGURE 4

Standard deviation versus average sampler package dust mass, all sampler packages for drill and dozer cabs.

determined as
2 2 ip
Sispatiat + cyclone) = (Sfinit ~ Steighing)

ia
Sispaal + eycloney = (0.072? — 0.0287)'/? = 0.066 mg

Using the value of 0.066 mg as an estimate of the remain-
ing variability, it is possible to re-plot the regression data as a
function of any desired weighing precision. Figure 5 shows the
limit values of the boundary data points with two regressions
for comparison. Whether the weighing precision is 0.020 mg or
an extreme value of 0.001 mg, Figure 5 and Table II show that
the summed dust mass spatial variability and cyclone variation,
even within an enclosed cab environment, is the dominant factor.

Figure 5 shows two data points represented by open dia-
monds. These points are a result of one filter weight (#2 of
the group of four in each of the two sampling packages) being
significantly different. However, no legitimate reason was doc-
umented to allow for the elimination of these points from the
analysis. The arrow tips indicate where the data points would be
located if the filter weight in question from each package were in
conformity with the other filter weights in their respective sam-
pler packages. Note, however, that movement of these points
would not affect the analysis appreciably. In fact, a re-run of the
regression changed the two curves by an almost imperceptible
amount.

This study did not directly determine the cyclone variabil-
ity. However, an estimated value previously reported can be

used to estimate by difference the dust mass spatial variabil-
ity component. Previous studies?" have reported CVcyctone tO
be 5 percent and 2 percent, respectively. Bartley et al.'') present
the most recent data on intercyclone variability. However, their
stated CVeyctone of 5 percent in the evaluation was based on parti-
cle count penetration of the cyclones at a fixed non-dust aerosol
concentration. Therefore, the relationship of CVcycione With con-
centration is not known. Using their more conservative value of
5 percent for the CVeycione, Single point evaluation of the stan-
dard deviation is 0.025 mg at 0.5 mg dust mass. Substituting this
value into Eq. 7 results in the equation

Sraiat + Seyctone = (0.066)"
Szyiqi + (0.025 mg)” = (0.066)?
Sopa = (0.062) mg

(8)

indicating that the cyclone variability is a negligible contribution
in comparison to the dust mass spatial variability.

Procedure B

Table II lists 107 valid samples obtained at outside locations
around eight different drills. CVoutside Was Calculated in the same
manner as in procedure A, with the exception that CVp7 was set
to the fixed value of 2.2 percent, as determined by extensive
replication in procedure A. The results are plotted in Figure 6.
Since,every dust sample of each sampler package was analyzed


The Effects of Low Quartz Mass Loading and Spatial
Variability on the Quartz Analysis of Surface Coal

Mine Dust Samples

Steven J. Page, John A. Organiscak, and Thomas Mal
National Institute for Occupational Safety Health, Pittsburgh, Pennsylvania

Monitoring the miner respirable quartz dust exposure for
compliance with U.S. federal regulations is mandatory to
protect the health of coal miners. However, there is no tech-
nique available for the direct measurement of the respirable
quartz dust concentration in the mining environment. The
only means to estimate the respirable quartz dust concen-
tration is to perform an analysis of the respirable dust sam-
ple collected, which contains both coal and quartz dust as
well as other mineral constituents. For samples collected at
coal mines, the U.S. Mine Safety and Health Administra-
tion (MSHA) utilizes the P7 method for respirable quartz
analysis.

The objectives of this work are to 1) evaluate the perfor-
mance of the P7 method on field samples of low quartz mass
loadings, 2) evaluate the magnitude of dust mass spatial vari-
ability within an enclosed cab environment, and 3) estimate
the subsequent propagation of error in calculating the per-
centage of quartz over a broad range of mass and quartz
filter loadings at a surface coal mine. These objectives were
achieved by obtaining surface coal mine dust samples over
a 9-month time period in enclosed cabs of three drills and
three bulldozers. By measuring weighing precision and using
reasonable estimates for sampler variability, the dust mass
spatial variability component was calculated by difference.
The results show that 1) the P7 method can be precise and
linear with quartz mass loadings as low as approximately
5 jmg of quartz, 2) the dust mass spatial variability, even
within an enclosed cab environment, is the dominant fac-
tor of variability in propagation of error in estimating the
quartz content of the obtained dust samples on a percentage
basis, and 3) weighing precision is not a significant factor in
error propagation. In addition to the enclosed cab samples,
samples from eight surface coal mine drills were collected
at various locations exterior to the drill cab. Significant data

This article is not subject to U.S. copyright laws.

910

scatter at these outside locations strongly suggests that the
ambient dust mass spatial variability has a significant effect
on the propagation of error in estimating quartz content of
the dust samples on a percentage basis.

Keywords Quartz, Surface Mining, Drilling

Crystalline silica (quartz) dust has long been recognized as
an occupational health hazard to the respiratory system,~® re-
sponsible for the insidious disease known as silicosis. In under-
ground mining, this quartz dust is created from several sources,
which generally include, but are not limited to, cutting, drilling,
or milling of rock material. In coal mining, this rock material
may be found as an inherent mineral matter constituent of the
coal, although this is usually not a significant source of quartz.
The majority of the quartz is found in the immediate overlaying
or underlaying rock strata that are frequently removed during the
coal mining process. Also, significant rock inclusions, known as
partings, occur within the coal seam itself and must of necessity
be removed with the coal. Surface mining operations can also
produce significant quantities of airborne respirable quartz due
to the amount of overburden that must be removed by means of
drilling, bulldozing, and haulage. In some instances, up to one
hundred meters of overburden must be removed to access ameter
of coal. In comparison with underground mining, the potential
for quartz exposure is much greater in surface mining opera-
tions. This potential quartz exposure is especially important for
drilling operations that involve crushing and pulverizing.

Monitoring the miner respirable dust exposure for compli-
ance with federal regulations is mandatory to protect the health
of coal miners. If the quartz content of the dust sample is mea-
sured to be 5 percent or less, the respirable dust standard is
2.0 mg/m’. If the quartz content exceeds 5 percent, the respirable
dust standard is reduced according to the formula

10mg/n_

percent quartz [30CFR 71.101]


Summary of outside samples and resultant coefficients of variation (CVs), assuming 2.2 percent

EFFECTS OF LOW QUARTZ MASS LOADING

TABLE I

CV for P7 analysis of each sample (Continued)

Net dust
weight, mg

Average

mg

Standard
deviation

mg

Standard
error mg

CV package

pet

+CVoutside _ —CVoutside

For pet quartz

pet

3.671
0.790
0.954
0.756
0.871
0.987
1.067
0.960
0.912
0.842
0.828
0.817
0.752
2.078
1.964
2.061
2.137
0.638
0.664
1.117
0.550
1.007
1.017
1.073
1.212
1.306
1.343
1.230
1.668
1,563
1.516
1.497
2.483
2.600
2.533
2.536
1.638
1.730
1.722
1.840
0.045
0.042
0.027
0.066
0.069
0.056

3.754

0.843

0.982

0.810

2.060

0.742

1,032

1.273

1,561

2.538

1.733

0.038

0.43

0.09

0.06

0.04

0.07

0.25

0.04

0.06

0.08

0.05

0.08

0.01

0.25

0.04

0.03

0.02

0.04

0.13

0.02

0.03

0.04

0.02

0.04

0.00

11.3

10.5

6.6

49

3.5

34.3

3.4

49

49

19

48

25.4

11.5

10.7

70

5.4

41

34.4

41

5.4

5.4

2.9

53

(Continued on next page)

-1L5

—10.7

25.5

921


920 S.J, PAGE ET AL.

TABLE I
Summary of outside samples and resultant coefficients of variation (CVs), assuming 2.2 percent
CV for P7 analysis of each sample

+CVoutside — —CVoutside

Standard

Net dust Average deviation © Standard == CVpackage For pet quartz
weight, mg mg mg error mg pet pet
0.182
0.509 0.346 0.23 0.16 66.9 67.0 —67.0
0.156 ;
0.286 0.221 0.09 0.06 41.6 417 417
0.458
0.432 0.445 0.02 0.01 4.1 47 47
0.354
0.350 0.352 0.00 0.00 0.8 24 -2.4
0.517
0.414 0.466 0.07 0.05 15.6 15.8 -15.8
0.418
0.420 0.419 0.00 0.00 0.3 2.3 -2.3
1.572
1.766 1.669 0.14 0.10 8.2 85. —8.5
1.122
1,302 1.212 0.13 0.09 10.5 10.7 -10.7
0.333
0.313 0.323 0.01 0.01 - 44 49 49
0.474
0.382 0.428 0.07 0.05 15.2 15.4 -15.4
0.602
0.459 0.531 0.10 0.07 19.1 19.2 —19.2
0.579
0.390 0.485 0.13 0.09 27.6 27.7 —27.7
0.321
0.407 0.364 0.06 0.04 16.7 16.9 —16.9
0.502
0.418 0.460 0.06 0.04 12.9 13.1 —13.1
0.574
0.996 0.785 0.30 0.21 38.0 38.1 —38.1
0.295 5
0.207 0.251 0.06 0.04 24.8 24.9 —249
0.237
0.225 0.231 0.01 0.01 3.7 43 —43
0.199
0.228 0,213 0.02 0.01 9.6 99 -9.9
2.533
2.294 2.414 0.17 0.12 7.0 14 -74
3.269
1.622 2.446 1.16 0.82 47.6 417 —477
0.714
0.687
0.780 0.727 0.05 0.03 6.6 7.0 -7.0
3.376
4.214

(Continued on next page)


EFFECTS OF LOW QUARTZ MASS LOADING 917

QUARTZ < 50 ug

70

8 °
¢ y = 1.03x + 0.49
2 ” 2
ES R? = 0.96 a
rz) 2 “0
Qo
ve
a“
< 20
2
o

10

0
° 5 10 15 20 28 30 35 40 45 50
QUARTZ, pg, single sample
FIGURE 2
P7 quartz analysis comparison of a single sample versus a composite of three remaining samples in the sampling package,
0 to 50 wg quartz.
180% CAB SAMPLES, DRILLS AND DOZERS
lo
o

100% 4

100%

-150%
SINGLE SAMPLE MASS, mg

FIGURE 3 .
Coefficient of variation (CV) versus single sample respirable dust mass, all sampler packages for drill and dozer cabs.


EFFECTS OF LOW QUARTZ MASS LOADING

Assumptions in the derivation of Eq.’3 are: iv) the errors rep-
resented by each term are multiplicative or divisional and not
additive; v) 0 for the individual components are sufficiently
small so that the dust mass concentration can be considered a
linear function in each range of error, for example, the mass
range (M + om); vi) the error terms are strictly independent.
Assumption (iv) can be validated from the equation used to cal-
culate C Vpackage?

Dust concentration, (mg/m*)
x Dustmass, (mg)
Pump flow rate, (L/min) * sampling time, (min)

4]

which shows the multiplicative/divisional nature of the terms
used in Eq. 3. In addition, it is assumed that any spatial vari-
ability associated with the quartz fraction of the airborne dust
is negligible in comparison to CVspaia for the total airborne
respirable dust. Quartz fraction spatial variability could not be
determined from this work due to the required sample destruc-
tion to obtain a single and composite quartz analysis for each
sampler package. Assumption (v) will be shown to be reason-
able in the Results section, due to the smallness of the standard
deviations presented. Assumption (vi), although not strictly true,
is sufficiently valid for the degree of precision required in this
work. For example, CVpump and CVeyctone are not strictly inde-
pendent in the sense that cyclone size-fractional dust penetration
is a function of the pump flow rate. However, the change in dust
concentration created by cyclone-to-cyclone variation can rea-
sonably be assumed to be significantly larger than the change
in dust concentration caused by fluctuations in pump flow rate.
Although not explicitly stated in Eq. 4, the cyclone variability
is multiplicative due to the definition of cyclone penetration,
ie., throughput/input. It is further assumed that errors in the
time measurement are so small in comparison to the other error
terms that they may be legitimately excluded.
Combining Eqs. 2 and 3, the sum of the terms

CV by + CV cighing + CVeyctone + CV ump

resulting from the appropriate substitutions, 1) represents the
method variability, 2) is independent of the term CVeoiats and
3) has been previously documented. The NIOSH Accuracy Cri-
terion“) requires that a sampling and analytical method be suf-
ficiently accurate such that measurements by the method will
come within 25 percent of the corresponding true air concen-
trations at least 95 percent of the time. In terms of the CV, a
method meets the NIOSH Accuracy Criterion if the true total
CVis equal to or less than 12.8 percent. Since it is not possible to
determine the “true” concentration or CY, it is usually required
that there be a 95 percent confidence that the above conditions
will be met. The P7 method was developed and was found to
be a NIOSH-classified B method meeting the above criteria in
a single laboratory.

One useful method of estimating the dust spatial variability
component is to empirically establish, with a large quantity of

913

data, an upper confidence limit for the standard deviation Siimit
associated with CVcap. Having obtained this value, it will be
shown that measured values for the remaining error terms Sx
can be subtracted from Simic according to the root-mean-square
(RMS) formula to estimate the dust spatial variability compo-
nent. Determination of Stimit can be accomplished by regression
analysis of the data. To analyze the data, it is necessary to note
that error values can assume any value within an absolute bound-
ary or upper limit defined only by an infinite number of measure-
ments. It is assumed in this analysis that the amount of data is
sufficient to allow for a reasonable approximation to this bound-
ary, analogous to an empirically derived upper confidence limit.
The appropriate strategy is to choose only the outermost points
of the data set as the best choice for the equation governing the
error propagation and the upper confidence limit. The method
chosen to evaluate the magnitude of the spatial variability of the
dust concentration in the enclosed cab environment is to perform
aregression of Eq. 2 using the respirable dust mass on each filter
analyzed for quartz as the independent variable. The equation
chosen for regression (SigmaPlot, SPSS, Inc., Chicago) is Eq. 2,
presented in the form

CVn = (cv Simit\?\
‘cab = | CVn + | 65]

CVp7 = P7 analytical replication coefficient of variation,
Siimit = estimated regression standard deviation, mg
x = respirable dust mass, mg, on filter analyzed
for quartz.

where

In this analysis, Sjimi is the regression constant of interest and
can be considered to represent an empirically derived upper con-
fidence limit to the net dust mass standard deviation due to all
variation sources. These include sampling pump variability, cy-
clone variability, weighing imprecision, and dust mass spatial
variability within the sampling environment (defined as the en-
closed cabs of the equipment). It will be seen in the Results
section that Simi can indeed be considered a constant. An ad-
ditional use of Eq. 5 is that, given estimates of other variability
components, a sensitivity analysis of weighing error can be eas-
ily performed. This will be done in the Results section.

Having determined Sjimi a8 a constant from the CV cap Tegres-
sion, the analysis is no longer bound to use CVs for the individ-
ual error terms in Eq. 3 for CVpackage in spite of the multiplica-
tive/divisional nature of these error terms. Given that 1) Stimit is
approximately constant, 2) the sampling pump flow rate error is
small in comparison to the other CV terms and can be ignored,
3) the standard deviations of cyclone penetration, weighing error,
and spatial variability are expressed in terms of mass changes,
and 4) the standard deviations are largely independent of mass
over the range of masses in the data, then Eq. 3 must be true
for all mass values and reduces to the RMS of the individual


912

calibration of all pumps had a CV of 0.5 percent. Sampling times
for all samples was greater than 420 minutes.

Sampling Procedures—Procedure B

For procedure B, samples from eight surface coal mine drills
were collected at various locations exterior to the drill cab. Sam-
pler packages consisted of either two, three, or four gravimetric
personal samplers mounted side-by-side on a tripod. Bach per-
sonal sampler consisted of a 10-mm Dorr Oliver nylon cyclone
and a Dupont Flowlite pump. Prior to sampling, each pump was
calibrated to within +5 percent of 2.0 L/min." However, cy-
clone and pump assignment was random with respect to position
and location. The samplers were used in dust source evaluations
of drills and therefore were moved as necessary to maintain po-
sition within the drill dust emissions. Sample times ranged from
73 to 475 minutes, depending on the severity of dust emissions.

SAMPLE ANALYSIS

After desiccation for 15 minutes and equilibration to weigh-
ing room conditions, each dust sample was pre- and post-
weighed on a Mettler M3 balance for mass gain. Weighing
room conditions were maintained at temperatures between 70
and 74°F, Relative humidity differences between pre- and post-
weighings varied between 0 and 30 percent. Because of these
fluctuations, a set of eight control filters was weighed weekly
over a 15-month period to estimate the weighing precision used
in the present analysis.

For procedure A, the number 2 sample of the four taken at
each location was analyzed by the P7 infrared method® for res-
pirable quartz at the NIOSH Pittsburgh Research Laboratory.
‘The number 2 sample was chosen so that the same cyclone sam-
pler was collecting the sample at the same location within the
cab and on the sampler rack, thus minimizing a source of unex-
plained variation. The remaining three dust samples were com-
bined into a composite sample before P7 analysis to determine
if a bias due to sample mass existed. The P7 analysis performed
at the NIOSH laboratory is periodically verified to be in agree-
ment with the P7 procedure used by MSHA. In addition, each
P7 analysis was replicated three times to estimate the precision
of the method.

For procedure B, P7 analysis was performed on each sample.
Because of the established precision of the P7 method, replicate
scans were performed only on certain filters for verification.

STATISTICAL METHODS

A common practice for reporting measured quartz mass is
as a percentage of the total respirable mass. However, since
mathematical operations performed with measured values re-
sult in propagation of error, any calculated value for percentage
of quartz is subject to propagation of error. For multiplicative
or divisional opérations, the propagation of error, in terms of

S.J. PAGE ET AL.

standard error (0), can be expressed as‘)
2 2 2
Om: ( Ima Fm
(@)-G)*G) om

oA
wn’ VN

Z=Z(A,B), A,B independent, measurable quantities.

On, = Ons =

After substitution and elimination of N, the number of samples,
it can be shown from the definition of CV that, for cab samples,

V2, = CV by + CVeackage (2)

where CVp7 and CVpackage are the coefficients of variation of the
quartz mass measurement and the respirable dust measurement,
respectively, in the calculated percentage of quartz. The same
formulation applies for procedure B samples outside of the cab,
yielding a measure for CVowside-

Several assumptions made in this derivation are i) 0, for
the quartz mass and dust mass measurements are sufficiently
small so that the quartz percentage can be considered a linear
function in the mass range (M + 0,,); ii) the measurements of the
quartz mass and dust mass are independent; iii) the errors in the
measurements of the quartz mass and dust mass are independent.
Assumption (i) will be shown in the Results section to be valid.
Itis important to note that this assumption does not need to apply
to either CVp7 or CVpackage Since these values, although useful
measures of error, are not the fundamental measures of error in
statistics. The CV of any measurement is a measure of relative,
not absolute, dispersion. As long as the original measure of error
(c or om) satisfies assumption (i), the derived equation for CVcab
is valid. Assumption (ii) is valid because there generally is no
relationship between the quartz mass and the dust mass from
test to test. These mass values are largely determined by the
geology, which can vary significantly from test to test and can be
considered random variations. Assumption (iii) usually follows
from assumption (ii). However, the validity of assumption (iii)
is assured in this work because the quartz mass and dust mass
are determined by strictly independent methods.

Given a package of four samplers, the total variation in the
mean of those samplers is a measured quantity. Knowing reliable
estimates for sampler pump, cyclone, weighing, and P7 analyt-
ical variation, the remaining variation due to dust mass spatial
variability is determined by difference. For the variations of the
sampling pump, cyclone sampler, gravimetric analysis, spatial
distribution of dust, and the P7 method for quartz mass measure-
ment associated with calculated values of percentage of quartz,
CVrackage Can be expressed as

CVachage = CVerighing + CVéyctone + CV

‘pump + Vip B)


EFFECTS OF LOW QUARTZ MASS LOADING

This formula establishes a limit for exposure to respirable coal
mine dust so that the concentration of quartz in the respirable
coal mine dust does not exceed 0.1 mg/m?. The formula uses a
common method for reporting measured quartz content, i.e., as a
percentage of the total respirable mass. However, mathematical
operations performed with measured values result in propaga-
tion of error. In other words, if a quantity V = V(x, y), then V
is in error by an amount dV as a result of the errors dx and dy
in the measured quantities x and y. Therefore, any calculated
value of the percentage of quartz is subject to propagation of
error.

Currently, no technique is available for the direct gravimetric
or real-time measurement of the respirable quartz dust concen-
tration in the mining environment. The only means to estimate
the respirable quartz dust concentration is to perform an analy-
sis of the respirable dust sample collected, which contains both
coal and quartz dust as well as other mineral constituents. The
techniques used for routine estimation of the quartz content of
a respirable dust sample,® recommended by the National In-
stitute for Occupational Safety and Health (NIOSH), are the
infrared Method 7603 and X-ray Method 7500. The Mine
Safety and Health Administration (MSHA) employs the P7 in-
frared method to estimate the quartz content of a respirable coal
mine dust sample.) The MSHA and NIOSH infrared meth-
ods are similar, but with some differences noted in the sample
preparation. Airborne dust samples from metal and nonmetal
mines are analyzed by MSHA using Method 7500. The recom-
mended weight gain range for coal mine quartz dust analysis
(P7) is between 0.5 and 2.0 mg, with a minimum quartz mass
of 25 ug.®) The original work (performed by SRI International
under contract to NIOSH) on which the method was based pro-
duced test samples of mixed dust which had no less than 70 jzg of
quartz. Mass loadings in that same study achieved light quartz
loadings down to 27 wg only when pure quartz material was
used. Thus, there has always been an issue about the perfor-
mance of the P7 method on field samples of low quartz mass
loadings.

To estimate the effect of dust mass spatial variability in the
calculation of quartz percentage in a variety of occupational set-
tings, it is informative to provide an estimate of the minimum
value the dust mass spatial variability can reasonably assume in
any environment. If this minimum value is known, then it can
be presumed that the effect of dust mass spatial variability will
be at least as great and, therefore, the calculation of quartz per-
centage will be affected accordingly. Sampling in underground
mine environments is not the best method to provide an esti-
mate of the effect of minimum dust mass spatial variability on
error propagation. Large dust concentration gradients may be
present due to a variety of causes, such as ventilation airflow
patterns and turbulence induced by water spray action and ma-
chine movement. Also, improvements to closely regulate and
control the volumetric flow rate of dust sampling pumps now al-
low for a more accurate estimate of minimum dust mass spatial
variability.

911

The objectives of this work are to 1) evaluate the performance
of the P7 method on field samples of low quartz mass loadings,
2) evaluate the magnitude of dust mass spatial variability within
an enclosed cab environment, and 3) estimate the subsequent
propagation of error in calculating the percentage of. quartz over
a broad range of mass and quartz filter loadings at a surface coal
mine. These objectives were achieved by obtaining surface coal
mine dust samples over a long-term time period in enclosed cabs
of three drills and three bulldozers.

EXPERIMENTAL METHODS

Two different sampling procedures were examined. Proce-
dure A was the weekly collection and P7 quartz analysis of
samples within enclosed cabs on drills and bulldozers at a mine
for a 9-month period, using very strictly controlled sampling
equipment. A surface coal mine located in West Virginia which
consistently operated blasthole drills and bulldozers equipped
with environmental cabs was selected for the study. The ro-
tary drills sampled were two Ingersoll-Rand DMSOQEs and a
DrillTech D40K. The bulldozers sampled were a Caterpillar
D9H, DION, and a D11N. Procedure B involved the collection
and P7 quartz analysis of samples taken outside of the cabs of
various drills at different mines under slightly less strict control
of the sampling equipment, as will be defined under Procedure B.

During both procedures, a broad range of weather conditions
were encountered because of seasonal variations. These condi-
tions consisted of 1) rain, snow, or no precipitation, 2) dry or
muddy ground conditions, and 3) ambient temperatures rang-
ing from 20 to 85°F. The effect of these environmental factors
was eliminated by calculating the standard deviations and co-
efficients of variation (CV, defined as the standard deviation
divided by the mean, x 100 percent) relative only to the mean of
simultaneously acquired samples.

Sampling Procedures—Procedure A

In procedure A, for one shift each week during the period
from November 1997 to August 1998, a sampling package, con-
sisting of four gravimetric personal samplers mounted side-by-
side on a rack, was located in the cab of one of three drills and
bulldozers each. Because of the small physical size of the cabs
on both the bulldozers and the Drill Techy drill, the sampler pack-
ages were located within approximately 2 feet of the operator’s
breathing zone. On the Ingersoll-Rand DMSOE drills, the cabs
were somewhat larger and the sampler-packages were located
within approximately 3 feet of the operator's breathing zone.

Each personal sampler consisted ‘of a 10-mm Dorr Oliver
nylon cyclone and a Dupont Flowlite pump. For each cyclone,
individual parts were numbered 80 that each exact cyclone would
be used with the same ‘nuiibered pump. Additionally,:each cy-
clone maintained its same respective position on therack on
which it was located. Each week prior to sampling, each pump
was calibrated under load (unused filter cassette + cyclone in a
sealed 1-gallon jar) to within +1 percent of 2.0 L/min. Overall,


922

S.J. PAGE ET AL.

TABLE II

Summary of outside samples and resultant coefficients of variation (CVs), assuming 2.2 percent
CV for P7 analysis of each sample (Continued)

+CVoutside _ —CVoutside

Standard

Net dust Average deviation Standard CVpackage For pet quartz
weight, mg mg mg error mg pet pet

0.073 0.066. 0.01 0.00 11.0 11.2 -11.2
0.132

0.136

0.135

0.117 0.130 0.01 0.00 6.8 72 7.2
0.187

0.152

0.138 0.159 0.03 0.01 15.9 16.0 -16.0
0.086

0.086

0.099

0.099 0.093 0.01 0.00 8.1 8.4 8.4
0.354

0.357

0.351

0.365 0.357 0.01 0.00 17 2.8 2.8

SURFACE MINE OUTSIDE SAMPLES
2,3,or 4 Samplers
150%

100%

Fi me
> a3,5° °
3 3.5 4 4s
60% 4 Ld
a 7 Sampler #2
5 Sampler #1
100% Sampler #4
@ Sampler #3
“150% SINGLE FILTER MASS, mg

FIGURE 6

Coefficient of variation (CV) versus single sample respirable dust mass, all outside drill sampler packages.