Deposition Uniformity of Coal Dust on Filters and Its Effect

on the Accuracy of FTIR Analyses for Silica

Arthur L. Miller,1 Pamela L. Drake,1 Nathaniel C. Murphy,1

Emanuele G. Cauda,2 Ryan F. LeBouf,3 and Gediminas Markevicius4

1

National Institute for Occupational Safety and Health, Spokane, Washington, USA

2

National Institute for Occupational Safety and Health, Pittsburgh, Pennsylvania, USA

3

National Institute for Occupational Safety and Health, Morgantown, West Virginia, USA

4

Applied Science Laboratory, Spokane, Washington, USA

Miners are exposed to silica-bearing dust which can lead to silicosis, a potentially fatal lung disease. Currently, airborne silica is

measured by collecting filter samples and sending them to a laboratory for analysis. Since this may take weeks, a field method is

needed to inform decisions aimed at reducing exposures. This study

investigates a field-portable Fourier transform infrared (FTIR)

method for end-of-shift (EOS) measurement of silica on filter samples. Since the method entails localized analyses, spatial uniformity

of dust deposition can affect accuracy and repeatability. The study,

therefore, assesses the influence of radial deposition uniformity on

the accuracy of the method. Using laboratory-generated Minusil

and coal dusts and three different types of sampling systems, multiple sets of filter samples were prepared. All samples were collected

in pairs to create parallel sets for training and validation. Silica

was measured by FTIR at nine locations across the face of each

filter and the data analyzed using a multiple regression analysis

technique that compared various models for predicting silica mass

on the filters using different numbers of “analysis shots.” It was

shown that deposition uniformity is independent of particle type

(kaolin vs. silica), which suggests the role of aerodynamic separation is negligible. Results also reflected the correlation between

the location and number of shots versus the predictive accuracy of

the models. The coefficient of variation (CV) for the models when

predicting mass of validation samples was 4%–51% depending on

the number of points analyzed and the type of sampler used, which

affected the uniformity of radial deposition on the filters. It was

shown that using a single shot at the center of the filter yielded

predictivity adequate for a field method, (93% return, CV approximately 15%) for samples collected with 3-piece cassettes.

Disclaimer: Mention of any company or product does not constitute

endorsement by the National Institute for Occupational Safety and

Health (NIOSH). The findings and conclusions in this report are those

of the authors and do not necessarily represent the views of the NIOSH.

The authors would like to thank Joe Archer and Jeanne Zimmer for

their invaluable assistance and attention to detail in preparing the many

filter samples required for this study.

Address correspondence to Arthur L. Miller, National Institute for

Occupational Safety and Health, Spokane Research Lab, 315 E. Montgomery Ave., Spokane, WA 99207, USA. E-mail: ALMiller@cdc.gov

1. INTRODUCTION

The National Institute for Occupational Safety and Health

(NIOSH) is investigating technologies for field-portable measurement of silica on filter samples of mine dust. That work

is motivated by the fact that inhalation of excessive amounts

of dust containing particles of crystalline silica can cause scar

tissue to form in the lungs, which reduces their ability to extract

oxygen from the air (DHHS(NIOSH) 1974). This condition is

called silicosis, which is a disabling, irreversible and sometimes

fatal lung disease. Each year, more than 250 American workers

die with silicosis (NIOSH 2012) and many of the deaths occur

in the mining industry (Bang et al. 2008). Despite extensive

knowledge regarding silicosis prevention (Cecala et al. 2012),

exposures are still common and have recently been linked to

a resurgence of coal workers’ pneumoconiosis (CWP) (Antao

et al. 2005; Laney and Attfield 2009; Suarthana et al. 2011).

The quantification of airborne silica has been studied previously with the goal of developing standard methods for determining worker exposures to silica-bearing dusts. Previous work

focused on developing methods for in-laboratory analysis of

filter samples taken in the field (Freedman et al. 1974; Ojima

2003; Ainsworth 2005). Miners’ exposure to silica is currently

determined in the United States by collecting a filter sample

and submitting it to the Mine Safety and Health Administration (MSHA), where it is analyzed by one of two methods. If

from a coal mine, the analysis entails an ashing and Fourier

transform infrared (FTIR) process known as the P7 analytical

method (MSHA 2008), while samples from noncoal mines are

analyzed using an X-ray diffraction technique (MSHA 1999).

Since these methods entail a time lag of weeks before exposure

data are received, the information is often of little use to inform
modifications to workplace conditions aimed at preventing overexposures.

It has recently been shown that a field-portable FTIR spectrometer could be used for relatively accurate analysis of filter

samples, with potential for use as an end-of-shift (EOS) method

(Miller et al. 2012). The limitations of such field-portable methods are that they are usually less sensitive than laboratory methods and are also not capable of analyzing the entire amount of

material on the filter, but rather depend on localized measurements or “shots” that include only a small portion of the filter

area (Chen et al. 2010; Miller et al. 2012). It is desirable that a potential EOS method produces results that are as close to the laboratory method as possible, while keeping the analytical method

simple and easy to use in the field. A portable method for filter

sample analysis should thus optimally entail a minimal number

of “shots,” where the shots are located to achieve adequate accuracy of predicting total silica mass on the filter. Thus, the ideal

field method would be analysis of a single shot at a location chosen to achieve the accuracy required. The method must also be

able to predict the total mass at different levels of dust loading,

which is known to affect the “deposition profile” across the filter.

The analysis of silica directly on collection media has been

previously investigated (Freedman et al. 1974; Kohyama and

Kurimori 1996; HSE 2005; Miller et al. 2012). Because it is

well known that filter samples are often not uniformly loaded

(Foster and Walker 1984; Morley et al. 1999; Harper et al.

2004), this study aims to determine how the nonuniformity of

dust deposition will influence the accuracy of a portable method.

In the study, findings are applied to measurements made with

an FTIR spectrometer but are expected to be relevant to other

portable methods as well.

The issue of spatial uniformity of filter deposition was assessed previously in conjunction with evaluation of analytical

protocols for asbestos (Lai et al. 2006). There are many factors

that affect spatial uniformity, some caused by particle deposition mechanisms including impaction, interception, settling, and

electrostatics. Spatial deposition of particles may also depend

on the design of the sampling system including factors such as

flow rate, standoff distance between the flow inlet and the filter

face (Vincent 1989; Baron et al. 1994; EPA 2008), or the design

of the filter backing plate, especially when “channels” are incorporated into the filter backing plate to help distribute the flow

across the filter (Jenkins et al. 1992). Closed face cassettes have

been shown to sometimes result in heavy center-loading (Harper

et al. 2004). This can be considerably lessened when a cyclone

preselector is used (Foster and Walker 1984), but the design

of some cyclone-based samplers has been shown to strongly

affect the results of on-filter analyses (Chen et al. 2010; Pretorius 2011). It is therefore difficult to extrapolate results derived

from different sampling trains. Additionally, the effect of filter

loading can be to increase the localized pressure drop across

the filter media, which in turn changes the airflow pattern and

influences deposition.

While variability of dust deposition is expected to be the primary source of error for on-filter analysis via portable methods,

other factors that may affect the results include variability in total deposition surface area (Noll et al. 2005), edge effects (Foster

and Walker 1984), or design of the backing plate that helps distribute the pressure across the filter (Jenkins et al. 1992). There

is also the possibility that various constituents of the dust may

have separated aerodynamically or that some larger particles

will have penetrated the cyclone (Lee et al. 2010) and deposited

preferentially (by impaction) near the center of the filter (Harper

et al. 2004), potentially leading to a slight particle size dependence that could affect mass distribution as well as FTIR results

(Foster and Walker 1984; Page 2003).

This article summarizes a study designed to determine the

effect of deposition uniformity on the accuracy of potential field

methods for filter sample analysis. The study was primarily focused on determining how the style of sampling cassette and

total dust loading contribute to uneven radial deposition of dust

particles on filter samples. In order to demonstrate how nonuniform deposition would affect the ability of a field-portable

method to predict total mass on the filter from localized measurements, the study included measurement of silica at multiple

locations on filter samples of coal dust using a field-portable

FTIR spectrometer. The data from these measurements were

used to develop models that predicted total mass on the filter

based on data from one or more measurement locations.

2. METHODS

For this study, filter samples of Min-U-Sil 5 (henceforth referred to as Minusil) and coal dusts were generated in a Marple

chamber using three different types of sampling trains, and

multiple sets of filter samples were collected at five different

dust loadings. Silica was measured by FTIR at nine locations

across the face of each filter and the data analyzed by a multiple

regression analysis (MRA) technique using statistical analysis

software, which employed varying numbers of FTIR data points

to predict the total mass on the filters. Results of the MRA were

used to compare the accuracy of various models (each employing different numbers of data points or “analysis shots”) for

predicting total dust mass on the filters.

The study was designed to limit the number of variables by

using the same sampling conditions that might be used for field

applications. Sampling setups, therefore, employed Dorr-Oliver

cyclone preselectors, and critical orifice flow controllers were

used to maintain the flow rates used in typical field samplers. All

samples for the study were collected onto preweighed 37-mm

polyvinylchloride (PVC) filters with 5-micron pore size (SKCR

Corp., Inc.). Three types of 37-mm plastic sample cassettes

were chosen; 3-piece, 2-piece, and the approved (MSA/Zefon)

tamper-proof cassettes used for respirable dust sampling in coal

mines. Using standard convention, the flow rate for the 2- and 3-

piece cassettes was 1.7 lpm, and for the MSA cassettes 2.0 lpm.

All three types have inlet and exit nipples located on center.

The MSA cassettes have the shortest inner distance between

inlet and outlet (8 mm), and the largest inner diameter of the

inlet (5 mm). The filter is suspended between the inlet and outlet,
together with a perforated backing pad, with the filter surface

approximately 4 mm from the inlet. The 2-piece cassettes have

a 9-mm inner distance and a 4-mm inlet diameter, while the 3-

piece cassettes have an 18-mm inner distance and a 4-mm inlet.

For both 2- and 3-piece cassettes, the filter and backing pad lie

on a plastic backing plate directly over the outlet and the air-flow

is pulled through small channels or “spokes” in the plastic plate.

The only difference between both 2- and 3-piece cassettes is the

distance from the inlet to the filter surface (8 mm for the 2-piece

and 17 mm for the 3-piece). Since all three cassette types are

radially symmetric in design, with inlet and outlet on center, the

analysis of deposition uniformity in the following sections was

based on the assumption of radial symmetry.

2.1. Filter Sample Preparation

Two types of dust were chosen for the study. For preliminary

tests, a standard silica reference material (Minusil) was chosen,

having a relative purity of 92% (Stacy et al. 2009). The coal dust

chosen for the study was derived from the Pittsburgh coal seam

and ground to a target mean diameter of 4 microns. It has been

characterized previously (Volkwein et al. 2004a) and contains

approximately 5.5% silica. More importantly, the silica content

is constant for this well-characterized, homogenous source of

coal dust, allowing for the correlation of FTIR data with total

dust mass as was done in this study.

Filter samples with known amounts of coal dust and Minusil

were prepared in a manner published previously (Miller et al.

2012). The dusts were aerosolized in a Marple chamber and

collected onto preweighed 37-mm PVC filters placed in each

of the three different cassette types. The filter loadings were

approximately in the range of 100–500 μg for Minusil and

250–3500 μg for coal dust. After collection, all filter samples

were post-weighed to determine the loaded mass of dust. Sets

of samples were prepared that included two filter samples (one

for training and one for validation) loaded at five different target

mass loadings for a total of 10 samples per set, plus three blanks.

Three sample sets (one for each of the three cassette types), were

prepared for both coal and Minusil, for a total of six sets.

2.2. Sample Analysis: FTIR

FTIR spectrometry entails illumination of the sample with

multiwavelength radiation using an IR-emitting source and an

interferometer. For the technique used in this study, the IR beam

is directed through the filter samples (a process referred to

as transmission mode). The beam interacts with the sample,

which absorbs certain wavelengths of radiation, and the transmitted radiation is collected by a detector and processed using

a Fourier transform algorithm, generating an “absorbance spectrum” (Griffiths and de Haseth 1986). The number of different

wavelengths generated by the interferometer determines the resolution of the spectrum, which is proportional to the path length

of the movable mirror in the interferometer. The absorbance

spectrum shows as to which wavelengths have been absorbed

by unique molecular bonds in the sample, and can thus be used

to quantify compounds containing those bonds. Since the scan

time is relatively short, the FTIR instrument typically conducts

multiple sequential scans, and the data from all scans are analyzed and averaged to reduce noise in the data.

The FTIR spectrometer chosen for this study (Bruker Optics,

model Alpha) was designed for field portability and is capable of

quantifying silica deposited on filters in the range of 25–200 μg

(Miller et al. 2012). In the Miller et al. study, the spectrometer

was calibrated using filter samples with known masses of a standard crystalline silica reference material deposited onto filters.

Such empirical calibration has been shown to be repeatable if

conducted carefully (DHHS(NIOSH) 1974). The resolution of

the FTIR can be adjusted by setting the path length of the mirror. For this study, the resolution was set to 4 cm−1

, which has

been shown previously to reduce noise (Tuchman 1992) while

still providing adequate peak identification and maintaining a

reasonably quick sampling time.

The method used for analyzing filter samples with the FTIR

instrument entailed mounting the filter in a stainless steel holder

that was placed so that the filter was centered between the horizontal IR source beam and the detector, where the beam has

a 6-mm focal diameter. The holder was vertically adjustable

in 3-mm increments to allow repeatable analyses at nine locations across the centerline of the filter. For the first shot, the

holder was placed so that the beam intersected the filter 12 mm

from the center. Subsequent shots were taken at 3-mm intervals

(center-to-center overlap of the 6-mm beam), with the fifth shot

at the filter center. At each location, the IR beam was turned

on for the time required to complete 40 scans, determined by

experimentation to be an adequate amount of sampling time to

ensure both a dependable signal-to-noise ratio and shot-to-shot

repeatability (Miller et al. 2012).

Prior to analyzing the Minusil or coal samples, scans of the

atmosphere and of the blank filter were run so that they could

be subtracted from the resulting silica and coal spectra. This

was done using the manufacturer’s software, and was necessary

to accurately quantify the silica peak. After the subtraction is

performed, the remaining spectra relates only to the Minusil or

coal that was on the filters.

For each shot, an absorbance spectrum was generated and

saved for analysis. Silica presented in the spectra as doublet

peaks between 816 cm−1 and 767 cm−1 and that region was

therefore integrated for all spectra to give an area (in arbitrary

units) representing the relative mass of silica at each of the nine

locations on the samples.

When measuring silica in coal dust using FTIR, it is usually

necessary to correct for interference by kaolin (Anderson 1983;

Ojima 2003; MSHA 2008). This is done by measuring the main

kaolin peak at approximately 915 cm−1 and using it along with a

correction scheme to adjust the value of the silica doublet peak

(Ainsworth 2005; Miller et al. 2012). However, in this study,

while the main kaolin peak was quantified (to investigate the

potential for a kaolin-specific deposition pattern), the correction

was not applied to the silica measurement. Instead, the raw
value of the silica doublet peak was correlated directly to the

gravimetric mass of the sample. The reasoning for this was

to eliminate potential errors in the data due to kaolin correction

itself, since our goal was to study the influence of only deposition

uniformity on the accuracy of using the FTIR method to predict

total mass on the filter.

Since kaolin correction will be applied to eventual field measurements of silica, it is important to know whether there is a

pattern in the deposition of silica-rich or kaolin-rich particles.

To ascertain this, the deposition profiles of both silica and kaolin

measurements were assessed independently.

2.3. Sample Analysis: EDS

To verify the accuracy and repeatability of the FTIR measurements, a subset of the Minusil and coal-dust samples were analyzed by energy-dispersive X-ray spectroscopy (EDS). An Oxford EDS system was used installed on an FEI Nova NanoSEM

230 Scanning Electron Microscope (SEM). Accelerating voltage was set to 20 kV and the spot size setting was 3.5.

In preparation for analysis, the filters were mounted onto 37-

mm stubs using carbon tape and sputter-coated with a 10-nm

layer of gold. The stubs were placed on the self-centering stage

of the SEM and the detector set at a working distance of 5 mm.

The magnification was set so that the EDS scan region was a

3-mm wide rectangle. Scan time was set to 20 s and the detector

recorded and stored 2000 channels of data in the energy range

from 0 to 20 keV.

For the sample subset, EDS analyses were done at the same

nine locations across each filter as for FTIR. During preliminary

testing, it was found that the deposition profiles have a relatively

uniform radial pattern but with some minor variations, depending on the orientation of the filter during analysis. For this reason,

a reference point specified as “top” was marked on all filters so

that both FTIR and EDS analyses would be taken at identical

locations across the filters. At each location, a spectrum was

generated that showed the relative abundance of Si, Al, and Cl

as markers for silica (SiO2), kaolin (Al2Si2O5(OH)4), and filter

background (PVC), respectively. These data were imported into

a spreadsheet and used to generate deposition profiles for each

element. In addition to verifying the accuracy and repeatability

of the FTIR measurements, these data were also used to evaluate

the potential for constituent-specific deposition bias.

2.4. Data Processing

The most important goal of this work was to quantify the

relative accuracy of predicting total filter loading using single

versus multiple localized FTIR analyses on a filter. This was accomplished by comparing predictive models that estimate total

filter deposition using FTIR data taken at one or more representative locations on the filter. Such data from sets of samples were

analyzed using a statistical approach to calculate how the choice

of number and location of the measurements affect the accuracy

of prediction when the FTIR-measured data from limited and

isolated locations are extrapolated to predict the total mass on

the filter.

A variety of models have been used previously for estimating

total filter deposition from measurements at localized points,

including models based on two points (EPA 2008), three points

(Morley et al. 1999), as well as area-weighted and distanceweighted locations (Lai et al. 2006). The method chosen for this

study entailed comparing models that incorporated one-to-four

radially unique points, and performing a multiple regression

analysis with no intercept (Morley et al. 1999) on each FTIR

dataset, using statistical analysis software (SAS Institute, Cary,

NC), with the total gravimetric mass of the filter sample as

the independent variable. The raw FTIR instrument readings

in arbitrary units were regressed against the known gravimetric

mass inμg/filter. No correction for the surface area analyzed was

necessary since the regression develops a predictive relationship

between the instrument readings and the gravimetric mass. It is

also assumed that the amount of silica and kaolin in the dust

is constant, which is a reasonable assumption for this wellcharacterized dust (Volkwein et al. 2004b).

Best-fit coefficients were determined for four-, three-, two-,

and one-point predictive models according to the following

equation:

Gravimetric mass = α1A + α2B + α3C + α4D, [1]

where α1 to α4 = coefficients for the four different models

(number of coefficients will match the number of locations used

in the model), and A–D are the measured peak areas at each

of the locations used in the regression (i.e., at the 3-mm-spaced

locations on the filter). Half of the 10 samples in each set

were used as a training set, and the results of Equation (1)

for the various model types were compared to the gravimetric

masses of the samples in order to develop calibrations (best fit

coefficients) using the various models. The calibrations were

then used to predict the mass of the validation samples to

determine the most appropriate model.

The center measurement was expected to be different from

the other positions, which would confirm the nonuniform deposition expected from closed-face cassette sampling. The locations that are radially equidistant from the center were not

expected to be different from each other (p value greater than

0.05), which would indicate radial symmetry of deposition.

The model results (predicted filter loading) were regressed

against gravimetric filter loading and the slopes, intercepts, and

Pearson correlation coefficients assessed. To analyze the relative

accuracy of each model across the five different filter loadings,

the percent return (predicted mass divided by gravimetric mass

times 100%) was calculated for each filter sample and averaged

for the set of five samples. That averaged value along with the

five individual values was used to calculate a relative standard

deviation (or coefficient of variation, CV) for each set of five

samples.
FIG. 1. “Averaged deposition profiles” of Minusil dust measured by FTIR

using combined data from five loadings for each cassette type.

,

3. RESULTS AND DISCUSSION

3.1. Filter Deposition: Silica

As a baseline for the study, and in order to avoid potential

errors due to issues such as the presence of kaolin, other potential confounders, or deposition bias of the constituents of coal

dust, we first studied deposition of pure silica (Minusil) on filters. Deposition profiles derived from FTIR analyses of Minusil

samples at the nine locations across the filters indicate that the

loading on the filters collected with 3-piece and 2-piece cassettes is predictably center-heavy and radially symmetric, while

deposition on filters collected with the MSA cassettes is less

predictable and less symmetric (Figure 1). While the shapes

of the profiles for the 2- and 3-piece cassettes were found to

be somewhat radially symmetric, that symmetry varied slightly,

indicating that the filters are each loaded slightly differently. To

better see trends (smooth out the sample-to-sample variation)

in the profiles, data at all five loadings were averaged for the

graph in Figure 1. It is notable that while the standoff distance to

the filter is shortest for the MSA cassettes (4 mm), compared to

8 mm and 17 mm for the 2- and 3-piece cassettes, there is no evidence of center-weighted deposition due to impaction as might

be expected. This suggests that the center-weighted loading observed with the other two cassettes may be more a function of

face velocity and pressure drop than of particle size and inertial

impaction. While the variation in mass per unit area across the

filters is relatively great for the 2- and 3-piece cassettes, the repeatability of the eccentricity of that deposition profile is more

important for purposes of predictivity (Foster and Walker 1984),

as will be demonstrated in the next section.

3.2. Filter Deposition: Coal

Since the “pressure drop profile” across the filter was expected to change with filter loading, the FTIR data for silica

analysis of the (more heavily loaded) coal samples were analyzed next. The results showed that the deposition profiles do

change slightly as filter loading increases (Figure 2). Note that

the data in Figure 2 have been normalized using the “top-most”

data point to allow for easier comparison of the different loadings. For the 2-piece cassettes (Figure 2b), it is apparent that as

the center portions of the filters become more heavily loaded,

the localized pressure drop increases and the loading shifts toward the outer regions, flattening the profile slightly. This effect is absent for the MSA cassettes due to the unique backing

plate design that allows broader distribution of the pressure drop

across the filter, and nearly so for the 3-piece cassettes, which

may be attributed to the better distribution of pressure in 3-piece

cassettes due to the larger plenum volume.

The averaged profiles for silica measurements in the coal

dust samples by FTIR were normalized for ease of comparison

to those of silicon by EDS (Figure 3). The validity of this comparison is based on the fact that the rim of each filter was marked

with a reference point to insure that analyses by FTIR and EDS

were done at the same locations. It is notable that while profiles

for the MSA cassettes tend to mirror each other (indicating that

the two measurement methods yield similar results), the EDS

profiles for the 2- and 3-piece cassettes both suggest a pattern

that is not evident in the FTIR data. This pattern appears to have

derived from the design of the backing plate for those cassettes,

which has a 2-mm-wide channel or “trench” located approximately 6 mm from the center (Jenkins et al. 1992). Since the

FTIR analytical region (6-mm-diameter spot) is larger than the

EDS scan region (3 × 3 mm rectangle), it does not reflect this

localized deposition anomaly. It is notable that such a deposition

anomaly has the potential to adversely affect on-filter analytical methods that employ highly localized measurements. The

high reading at the 9-mm location for the MSA cassette data is

an example of another localized anomaly, but in this case it is

unique to this dataset and reflects random variations in localized

deposition, as suggested in Figure 2.

The three main constituents of dust in a coal-mine atmosphere are typically coal, silica, and kaolin (Anderson 1983;

Schatzel 2009). While data regarding the relative particle size

and morphology of these constituents is limited, it is generally assumed that they are well mixed, making each coal dust

unique from the perspective of particle size distribution and elemental composition (Schatzel 2009). This being the case, it is

sometimes assumed that individual constituents will not separate

aerodynamically during collection. To evaluate the possibility

of a compound or constituent-specific pattern in the material

deposition, the averaged deposition profiles for both kaolin and

(kaolin-corrected) silica in coal-dust samples (3-piece data only)

were compared. The comparison suggests that no discernible

constituent-dependent deposition is occurring in 3-piece cassettes for the coal dust used in this study (Figure 4).

To further confirm the lack of constituent-dependent deposition, the samples used to generate Figure 4 were subsequently

analyzed by EDS. The resulting deposition profiles showing
FIG. 2. Deposition profiles for coal dust at five mass loadings, measured by

FTIR on filters collected with (a) 3-piece, (b) 2-piece, and (c) MSA cassettes,

and normalized to the top-most measurement location on the filters.

FIG. 3. Averaged deposition profiles for silica and silicon, measured on three

sets of five coal samples loaded in the range 250–3500 μg/filter, by FTIR and

EDS, respectively.

relative abundance of Al and Si across the filters, representing

markers for silica and kaolin, were compared (Figure 5), and the

comparisons indicate strong correlation between the deposition

profiles for the two elements, similar to those above for FTIR.

3.3. Effect of Deposition on Predictivity of Mass Loading

Using Coal Dust

The radial symmetry of profiles for the 2- and 3-piece cassette

data and the random deposition of dust on the MSA cassette

samples both support the assumption that a statistical model

needs only to include radially unique points. We, therefore, limited our assessment to predictive models that took into account

FIG. 4. Averaged deposition profiles measured by FTIR for silica (800 cm−1

peak, kaolin-corrected) and kaolin (915 cm−1 peak).
FIG. 5. Averaged (and normalized) deposition profiles for Si and Al in coal

dust samples, measured by EDS. FIG. 6. Average CVs for the five loadings versus number of points in the

model for each of the 3-cassette types. Error bars are standard deviations based

on CVs of multiple models with same number of points.

one to four radially unique points located at 3-mm increments

from the center to the periphery. Using data from each of the

(one to four) unique locations on sets of five filters (five loadings), multiple regression models were created, with the total

gravimetric loading as the independent variable. Results from

the various models indicate that using more data points is not

significantly advantageous, and that the accuracy of prediction

(reflected by the % return) for all data sets and all models varied

from about 75% to 125%. For brevity, results from only a few

selected models are presented (Table 1).

The CV values for multiple models with the same number

of points were used to calculate an average CV value, representative of models with that number of points, and a standard

deviation based on the multiple CV values. The relation between

precision of prediction (CV) and number of points used for the

models (Figure 6) suggests that multiple-point models do not

give significantly better precision. Since the models represent

a best-fit for the five different loadings, a key factor affecting

accuracy and precision of predictability is the potential for variation of deposition with filter loading. It is notable that the CVs

for the MSA cassettes are highest, reflective of the randomness

of the deposition evident in Figure 2. The CVs for the 3-piece

cassette data are the most consistent, judging by the trend in

the CVs using different models (Figure 6). The CVs for the

2-piece cassettes vary slightly more than those for the 3-piece

cassettes, which may be related to the fact that the FTIR data for

the coal dust (Figure 2) indicate that the 2-piece cassettes have

the most variability in eccentricity of the deposition profile with

increased loading.

TABLE 1

Compiled data (average of all five loadings) from selected predictive models based on the multiple regression analysis technique

2-pc 3-pc MSA 2-pc 3-pc MSA 2-pc 3-pc MSA

Location 1-point – 12 mm 1-point – 18 mm 1-point –24 mm

% Return 99.1 86.9 75.7 97.7 88.9 122.7 93.4 90.5 88.2

CV (%) 9.5 14.6 51.2 12.6 13.3 23.3 13.4 18.4 28.7

Location 2-point–12 mm, 24 mm 2-point–18 mm, 24 mm 2-point–9 mm, 24 mm

% Return 95.5 80.2 81.7 91.1 86.7 92.9 95.2 90.3 83.9

CV (%) 11.7 21.9 39.4 24.2 9.4 27.6 13.1 16.2 16.3

Location 3-point – 12 mm, 18 mm, 24 mm 3-point – 12 mm, 6 mm, 24 mm 3-point – 12 mm, 18 mm, 0 mm

% Return 99.0 80.5 92.1 80.7 98.2 85.0 103.7 85.1 90.6

CV (%) 4.1 18.4 37.9 6.6 21.2 30.4 6.9 22.0 42.5

Location 4-point–0 mm, 6 mm, 12 mm, 18 mm 4-point–6 mm, 12 mm, 18 mm, 24 mm 4-point–0 mm, 6 mm, 15 mm, 21 mm

% Return 103.6 90.0 80.6 97.3 81.2 111.9 121.5 83.0 95.7

CV (%) 7.2 14.4 49.1 9.2 17.2 56.5 21.9 13.4 25.1
It is visually apparent (Figure 2) that the profiles for 2- and 3-

piece cassettes are radially symmetric, but with some variation

from sample to sample, which would be expected to introduce

some error in the predictability of the various models. With this

in mind, we assessed the single-point models using data from

the center and each of the four radially unique locations. For

all locations except the center shot, data from the two radially

symmetric points were averaged. It was found that for the 2-

and 3-piece cassettes, the CV had a very slight increasing trend

for locations away from the center point (Figure 7), while for

the MSA filters, the CV followed no trend. It is expected that

other samples would follow the trends suggested by this limited

dataset, suggesting that a single shot in the center of the filter

would provide necessary precision for a field method when

using 2- or 3-piece cassettes. Judging by the upper bound of

the error for the dataset of Figure 6, single-point models for

the 3-cassettes types would be expected to yield coefficients of

variation less than about 18% for 3-piece, 15% for 2-piece, and

50% for MSA cassettes.

Results from multiple regression analyses of single FTIR

shots taken at multiple locations on coal dust samples from the

three different types of cassettes (training samples) provided

coefficients (Equation (1)) which were then used to predict the

mass of dust on a separate set of validation samples. These

results demonstrate the good correlation possible with this approach (Figure 8). The accuracy is indicated by average percent

(%) returns of 99.2, 93.3, and 89.5% for 2-piece, 3-piece, and

MSA cassettes, respectively. While the percent (%) return data

for the 3-piece and MSA cassettes (Figure 8a and c) suggest

poor correlation between predicted and measured data at the

lowest loading, this is presumably because for light loading,

FIG. 7. CVs (based on sets of five samples) for five different single-point

models (note that for the noncenter points, data from two radially symmetric

points were averaged for this graph).

FIG. 8. Results of multiple regression analyses on coal dust for the (center)

one-point models for: (a) 3-piece, (b) 2-piece, and (c) MSA cassette types.
small measurement errors (such as those inherent to gravimetric

measurements) lead to relatively large differences between estimations and measurements. More importantly, for all cassette

types there is much better correlation for the filters loaded at or

above 500 μg, commensurate with realistic loadings expected

for field samples. The y-intercepts of the plots in Figure 8a–c are

all negative, reflecting that the models do not represent/predict

low loadings well. However, the slopes are all near unity, which

suggests that the models accurately estimate the majority of

loadings.

Using the data from Figure 8, which are the estimated loadings of unknown coal-dust samples using the coefficients from

SAS calculations, the CVs for the estimations were calculated

as 15%, 9%, and 51% for the 3-piece, 2-piece, and MSA data,

respectively. It is notable that the very high CV value for the

MSA data is likely a result of the anomalous percent (%) return

(predicted/measured) value at the lowest loading for that case

(Figure 8c).

4. CONCLUSIONS

This study provided data that demonstrate the role of filter

deposition uniformity when using a field-portable method to

make localized measurements on filter samples, such as when

measuring the mass of silica on filter samples of coal dust using

an FTIR spectrometer. The following findings are relevant:

• When samples are collected using 2-piece and 3-piece

cassettes at a flow rate of 1.7 lpm, dust deposition

is relatively radially symmetric and approximately

1.4–2.6 times higher near the center of the cassettes.

• For samples collected on MSA-style cassettes at 2.0

lpm, deposition is variable across the filter and no discernible pattern is evident.

• The deposition uniformity is independent of particle

type (kaolin vs. silica), which suggests the role of aerodynamic separation is negligible.

• For lab-generated coal-dust samples on 37-mm PVC

filters, the predictive accuracy as reflected by percent

(%) return of total mass using FTIR silica measurements varied from about 75% to 125% depending on

the sampler and the number of measured points used

in the predictive model.

• The current method in use—predicting total mass

on filters from plastic cassettes using a single shot

analysis—was shown to be reasonably precise, with

average CVs of 12% and 15% for 2-piece and 3-piece

cassettes, respectively (Figure 6).

• For this limited dataset, a single-point measurement

at the center resulted in a percent (%) return value of

99.2%, 93.3%, and 89.5% for 2-piece, 3-piece, and

MSA cassettes, respectively.

• For field applications, accuracy can potentially be improved by choosing measurement locations that optimize the predictivity, based on statistical analysis of

many samples. Accuracy may also be improved by

fitting a parametric distribution to the radial profile.

Results indicate that deposition uniformity has an effect on

the accuracy of predicting total mass on a filter sample using localized analyses, and data were presented to quantify the

effect for samples collected with three types of 37-mm filter cassettes. The approach demonstrated here could serve as a guide

while conducting in-mine evaluations of miners’ exposure to

silica dust, using FTIR data based on the analysis of 37-mm

filter samples. Such work should consider a similar approach,

to properly develop a predictive model using an appropriate

number of shots and their locations, taking into account the required accuracy. For measuring silica on filter samples at EOS,

it appears that a single shot at the center of the filter could provide data of accuracy acceptable for a field method, with the

exception of lightly loaded samples (less than about 500 μg) on

MSA cassettes. For that case, a multiple-shot approach may be

necessary to accurately predict silica mass. Continuation of this

research will include field demonstrations in which EOS silica

measurement will be conducted in active mines via analysis of

37-mm filter samples using a portable FTIR instrument.

REFERENCES

Ainsworth, S., and M. (2005). Infrared Analysis of Respirable Coal Mine Dust

for Quartz: Thirty Five Years. J. ASTM Int., 2:13.

Anderson, C. C. (1983). Collaborative Tests of Two Methods for Determining

Free Silica in Airborne Dust. DHHS (NIOSH) Publ. No. 83–124:157.

Antao, V. C., Petsonk, E. L., Sokolow, L. Z., Wolfe, A. L., Pinheiro, G. A.,

Hals, J. M., and Attfield, M. D. (2005). Rapidly Progressive Coal Workers’

Pneumoconiosis in the United States: Geographic Clustering and Other

Factors. Occup. Environ. Med., 62:670–674.

Bang, K., Attfield, M., Wood, J., and Syamlal, G. (2008). National Trends

in Silicosis Mortality in the United States, 1981–2004. Am. J. Ind. Med.,

51:633–639.

Baron, P. A., Chen, C.-C., Hemenway, D. R., and O’Shaughnessy, P. (1994).

Non-Uniform Air Flow in Inlets: The Effect on Filter Deposits in the Fiber

Sampling Cassette. Am. Ind. Hyg. Assoc., 55:722–732.

Cecala, A., O’Brien, A., Schall, J., Colinet, J., Fox, W., Franta, R., Joy, J., WR,

R., Reeser, P., Rounds, J., and Schultz, M. (2012). Dust Control Handbook

for Industrial Minerals Mining and Processing. RI 9689:NIOSH Pub. No.

2012–112:284.

Chen, C. H., Tsaia, P. J., Lai, C. Y., Peng, Y. L., Soo, J. C., Chen, C. Y., and Shih,

T. S. (2010). Effects of Uniformities of Deposition of Respirable Particles

on Filters on Determining Their Quartz Contents by using the Direct onFilter X-Ray Diffraction (DOF XRD) Method. J. Hazard. Mater., 176:389–

394.

DHHS(NIOSH) (1974). Criteria for a Recommended Standard: Occupational

Exposure to Crystalline Silica. Publ. No. 75–120:120.

EPA (2008). Laboratory determination of particle deposition uniformity on filters collected using federal reference method samplers. EPA-Office R&D:27.

Foster, R. D., and Walker, R. F. (1984). Quantitative Determination of Crystalline Silica in Respirable Size Dust Samples by Infrared Spectrophotometry. Analyst, 109:1117–1127.

Freedman, R. W., Toma, S. Z., and Lang, H. W. (1974). On-Filter Analysis of

Quartz in Respirable Coal Dust by Infrared Absorption and X-Ray Diffraction. Am. Ind. Hyg. Assoc. J., 35:411–418.

Griffiths, P. R., and de Haseth, J. A., eds. (1986). Fourier Transform Infrared

Spectrometry. John Wiley & Sons, New York.
Harper, M., Hallmark, T. S., Andrew, M. E., and Bird, A. J. (2004). A Comparison of X-Ray Fluorescence and Wet Chemical Analysis of Air Filter Samples from a Scrap Lead Melting Operation. J. Environ. Monit., 6:

819–826.

HSE (2005). MDHS 101 - Crystalline Silica in Respirable Airborne Dusts:

Direct-On-Filter Analyses by Infrared Spectroscopy and X-Ray Diffraction.

HSE - Health and Safety Executive.

Jenkins, S. L., Feigley, C. E., and Jackson, K. L. (1992). Fluorometric Analysis of the Uniformity of Deposition on Cassette Membrane Filters. Appl.

Occupat. Environ. Hyg., 7:665–671.

Kohyama, N., and Kurimori, S. (1996). A Total Sample Preparation Method

for the Measurement of Airborne Asbestos and Other Fibers by Optical and

Electron Microscopy. Ind. Health, 34:185–203.

Lai, C. Y., Kuo, Y. M., Hwang, J. S., Shih, T. S., and Chen, C. C. (2006).

Determination of Uniformity of Filter Deposits. Aerosol Sci. Technol. 40:

607–614.

Laney, A. S., and Attfield, M. D. (2009). Quartz Exposure Can Cause Pneumoconiosis in Coal Workers. J. Occup. Environ. Med., 51:868.

Lee, T., Kim, S. W., Chisholm, W. P., Slaven, J., and Harper, M. (2010). Performance of High Flow Rate Samplers for Respirable Particle Collection. Ann.

Occup. Hyg., 54:697–709.

Miller, A. L., Drake, P. L., Murphy, N. C., Noll, J. D., and Volkwein, J. C. (2012).

Evaluating Portable Infrared Spectrometers for Measuring the Silica Content

of Coal Dust. J. Environ. Monit.,

Morley, C. J., Clark, S. C., Deddens, J. A., Ashley, K., and Roda, S. (1999).

Evaluation of a Portable X-Ray Fluorescence Instrument for the Determination of Lead in Workplace Air Samples. Appl. Occupat. Environ. Hyg.,

14:306–316.

MSHA (1999). X-Ray Diffraction Determination of Quartz and Cristobalite in

Respirable Mine Dust: Method P-2. U.S. Department of Labor, Mine Safety

and Health Administration, Pittsburgh, PA.

MSHA (2008). Infrared Determination of Quartz in Respirable Coal Mine Dust -

Method No. MSHA P7. US Department of Labor-MSHA-Pittsburgh Safety

and Health Technology Center.

NIOSH (2012). Work-Related Lung Disease Surveillance System (eWoRLD)

[Ref.No. 2012F03-01] CDC/NIOSH. Available at: ≤http://www2a.cdc.gov/

drds/WorldReportData/> [Accessed Date:April 2012].

Noll, J. D., Timko, R. J., McWilliams, L., Hall, P., and Haney, R. (2005).

Sampling Results of the Improved SKC Diesel Particulate Matter Cassette.

J. Occup. Environ. Hyg., 2:29–37.

Ojima, J. (2003). Determining of Crystalline Silica in Respirable Dust Samples

by Infrared Spectrophotometry in the Presence of Interferences. J. Occup.

Health., 45:94–103.

Page, S. J. (2003). Comparison of Coal Mine Dust Size Distributions and Calibration Standards for Crystalline Silica Analysis. Aiha J., 64:30–39.

Pretorius, C. J. (2011). The Effect of Size-selective Samplers (Cyclones) on

XRD Response. J. Mine Ventil. Soc. South Africa, 64.3:8–12.

Schatzel, S. J. (2009). Identifying Sources of Respirable Quartz and Silica Dust

in Underground Coal Mines in Southern West Virginia, Western Virginia,

and Eastern Kentucky. Int. J. Coal Geol., 78:110–118.

Stacy, P., Kauffer, E., Moulut, J.-C., Dion, C., Beauparlant, M., Fernandez,

P., Key-Schwartz, R., Friede, B., and Wake, D. (2009). An International

Comparison of the Crystallinity of Calibration Materials for the Analysis of

Respirable Alpha Quartz Using X-Ray Diffraction and a Comparison with

Results from the Infrared KBr Disc Method. Ann. Occup. Hyg., 53:639–

649.

Suarthana, E., Laney, A. S., Storey, E., Hale, J. M., and Attfield, M. D. (2011).

Coal Workers’ Pneumoconiosis in the United States: Regional Differences

40 Years After Implementation of the 1969 Federal Coal Mine Health and

Safety Act. Occup. Environ. Med., 68:6.

Tuchman, D. P. (1992). Research Toward Direct Analysis of Quartz Dust

on Filters Using FTIR Spectroscopy. US Department of Interior (BOM),

IC9309:17.

Vincent, J. H. (1989). Aerosol Sampling: Science, Standards, Instrumentation,

and Applications. John Wiley & Sons, Ltd., Chisester, UK.

Volkwein, J. C., Vinson, R. P., McWilliams, L. J., Tuchman, D. P., and Mischler,

S. E. (2004a). Performance of a New Personal Respirable Dust Monitor for

Mine Use. DHHS (NIOSH) Publ. No. 2004–151, RI 9663:20.

Volkwein, J. C., Vinson, R. P., McWilliams, L. J., Tuchman, D. P. and Mischler,

S. E. (2004b). Performance of a New Personal Respirable Dust Monitor for

Mine Use. DHHS (NIOSH) Publication No. 2004-151, Report of Investigations 9663, 2004 Jun :1–25., ed. National Institute for Occupational Safety

and Health, US Department of Health and Human Services, Centers for

Disease Control and Prevention, Pittsburg, PA.