Evaluating portable infrared spectrometers for measuring the silica content of

coal dust

Arthur L. Miller,*a Pamela L. Drake,a Nathaniel C. Murphy,a

a

National Institute for Occupational Safety and Health, 315 E.

Montgomery Ave, Spokane, WA, 99207. E-mail: ALMiller@cdc.gov

James D. Nollb and Jon C. Volkweinb

b

National Institute for Occupational Safety and Health, 626 Cochrans Mill

Rd, Pittsburgh, PA, 15236

Environmental impact

This study focuses on the evaluation of human exposure to silica in working environments using an alternate field-portable method

for quantifying silica. Current methods for workplace silica monitoring typically involve estimation of time-weighted average silica

concentration using a traditional filter sampling system followed by laboratory analysis of the filter samples. However, since this

entails a time lag of days to weeks before exposure data are received, the information is often of limited value for helping reduce

exposures. The field portable infrared spectrometry method evaluated in this study is capable of on-site quantification of silica,

thereby providing timely feedback to inform workplace modifications to reduce exposures to airborne silica dust.

Miners face a variety of respiratory hazards while on the job, including exposure to silica dust which

can lead to silicosis, a potentially fatal lung disease. Currently, field-collected filter samples of silica are

sent for laboratory analysis and the results take weeks to be reported. Since the mining workplace is

constantly moving into new and often different geological strata with changing silica levels, more timely

data on silica levels in mining workplaces could help reduce exposures. Improvements in infrared (IR)

spectroscopy open the prospect for end-of-shift silica measurements at mine sites. Two field-portable

IR spectrometers were evaluated for their ability to quantify the mass of silica on filter samples loaded

with known amounts of either silica or silica-bearing coal dust (silica content ranging from 10–200 mg/

filter). Analyses included a scheme to correct for the presence of kaolin, which is a confounder for IR

analysis of silica. IR measurements of the samples were compared to parallel measurements derived

using the laboratory-based U.S. Mine Safety and Health Administration P7 analytical method. Linear

correlations between Fourier transform infrared (FTIR) and P7 data yielded slopes in the range of

0.90–0.97 with minimal bias. Data from a variable filter array spectrometer did not correlate as well,

mainly due to poor wavelength resolution compared to the FTIR instrument. This work has shown that

FTIR spectrometry has the potential to reasonably estimate the silica exposure of miners if employed in

an end-of-shift method.

Introduction

Inhalation of excessive amounts of dust that contains microscopic particles of crystalline silica can cause scar tissue to form

in the lungs, which reduces their ability to extract oxygen from

the air.1 This condition is called silicosis, which is a disabling,

irreversible, and sometimes fatal lung disease. National Institute

for Occupational Safety and Health (NIOSH) studies have been

important in documenting the extent of silicosis in industrial and

occupational settings over the last 35 years.2 Between the late 80s

and mid-90s, silicosis, excluding carcinomas potentially caused

by silica, prematurely shortened thousands of American miners’

lives. Presently, more than 1 million U.S. workers are routinely

exposed to crystalline silica, and each year more than 250

American workers die with silicosis. Between 1996 and 1999,

a quarter of all silicosis-related deaths occurred in the mining

industry.2 Further, a study in 2003 suggested that mortality data

may underestimate the incidence of silicosis, since only approximately 1 in 6 death certificates of people who died with silicosis

made mention of it as a cause of death.3

Despite extensive knowledge of both causes and effective

preventive actions, silica exposures in many occupational settings

continue. Risks are particularly high in the mining industry, in

both coal and non-coal miners.2 Exposure to silica-bearing coal

dust can lead to silicosis and coal workers’ pneumoconiosis
(CWP), both of which are a result of lung damage caused by

fibrosis. Much research has been done to reduce CWP, but

recently an increase in cases of CWP occurring in the U.S. has

been identified4 and silica may be implicated in that resurgence.

To help reduce exposure to both dust and silica, NIOSH

developed a direct reading monitor for measuring miners’

exposure to coal dust.5,6 This personal dust monitor (PDM) has

been successfully used to aid miners in reducing their exposure to

coal dust by making changes to their work activities based on the

continuous reading of the device7 but does not provide information specific to silica exposure. Coal miners’ exposure to silica

is currently determined in the U.S. by collecting a filter sample

and submitting it to the Mine Safety and Health Administration

(MSHA) where it is analyzed by an ashing and Fourier transform

infrared (FTIR) process known as the P7 analytical method.8

Since this method entails 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. Based on the success of the PDM in reducing

miners exposure to coal dust,7 NIOSH is now taking a similar

approach, specifically in regard to silica monitoring. The goal of

this initial work is to evaluate spectrometers for end-of-shift

(EOS) silica measurement on coal dust filter samples. Such EOS

data would give miners timely feedback regarding whether silicabearing strata had been encountered, preventing potentially

prolonged periods of overexposure prior to receiving analytical

results. This paper summarizes the evaluation of two fieldportable infrared (IR) spectrometers with the potential for EOS

quantification of silica on filter samples of coal dust.

Instrument considerations

The quantification of silica on filter samples has been studied

extensively, with the goal of developing standard methods for

determining worker exposures to airborne silica-bearing dusts.

Historical work focused on developing methods for in-laboratory analysis of samples taken in the field.9–11 It was found that

IR techniques were amenable to quantification of filter samples if

the samples were first ashed and a laboratory-grade FTIR

spectrometer was used to analyze the ash. The current work

expands on past efforts and aims at evaluating field-portable IR

instruments for measuring silica directly on filter samples (a nonashing approach). The idea is to take advantage of improvements

such as instrumentation miniaturization and spectral interpretation software to enable the use of relatively sophisticated

techniques in field settings.

The first challenge in applying field-portable spectrometry

methods is that they are inherently less sensitive than their labbased or benchtop counterparts. Respirable coal dust samples

from mines are required to be below 5% quartz in a 2 mg full shift

sample, which effectively limits the respirable quartz exposure to

100 mg per shift.12 Any proposed silica quantification method

must therefore be capable of accurately measuring small mass

loadings of silica (down to about 25 mg/filter) on filter samples

containing about 500–2000 mg/filter of coal dust. To simplify

a potential EOS method, it is desired that this be done on the

filters as collected, i.e. without treatment or redeposition as

described previously.10

A second challenge is that in order to be of use, the portable

method must be able to quantify silica in the presence of

compounds that potentially interfere with quantification of the

free silica on the filter. While more than a dozen minerals exist

with IR-absorption bands that could potentially interfere with

silica quantification,11 it is expected that kaolin will be the

primary interferent for coal dust samples based on MSHA’s

previous work.9 Kaolinite clay, or kaolin (Al2Si2O5(OH)4), is

a layered silicate material that can be found in mines along with

coal and silica. Since it contains silica as part of its composition,

it has the potential to interfere with IR spectrometry methods

and a correction must therefore be made to account for it. The

kaolin correction scheme utilized in this study is described in the

materials and methods section.

IR spectrometry is based on the principle that many different

chemical compounds preferentially absorb IR radiation of

a specific wavelength that is harmonic with the vibration

frequency of their molecular bonding structure.13 The spectrometer measures which frequencies of radiation get absorbed

by the sample and generates a unique absorption spectrum for

that sample, which is used to identify compounds and thus to

investigate sample composition. IR is well suited to measure the

alpha quartz polymorph of silicon dioxide. Unlike elemental

analyses, such as X-ray diffraction or laser-induced breakdown

spectroscopy, IR measurement is based on the SiO2 bond

structure, enabling it to distinguish between amorphous and

crystalline forms of silica.

The two spectrometers chosen for this study were an FTIR

spectrometer and a variable filter array (VFA) spectrometer.

FTIR is a spectrometry method whereby the sample is illuminated simultaneously with radiation of many wavelengths over

a wide spectral range by using a blackbody IR emitting source

and an interferometer, and the return signal analyzed using

a Fourier transform algorithm.14 The number of different

wavelengths generated by the interferometer determines the

resolution of the spectra, which is inversely proportional to the

mirror speed (scan time). Better resolution and wider spectral

range are both benefits to using FTIR over VFA. Since the scan

time is relatively short, the FTIR instrument typically conducts

multiple sequential tests and the data from all tests are analyzed

and averaged to reduce noise in the data.

VFA technology is based on the use of a linear variable filter

(LVF). An LVF is a wedge-shaped filter that works similarly to

a prism for visible light. An LVF typically covers approximately

an octave in wavelength, and the proper octave is chosen to

match that of the source beam used. The resolution is a function

of the LVF and detector characteristics. Some VFA instruments

employ a multi-pixel detector array, and in that case the

maximum theoretical resolution is limited by the number of

pixels in the array and by thermal and optical interference from

pixel to pixel.15

It is notable that both FTIR and VFA-IR methods can be

performed in two different modes: transmission mode or attenuated total reflection (ATR) mode. Since ATR requires physical

contact with the sample and our analyses were required to be

non-destructive to the filter samples, it was not used in this study.

It is preferable for IR spectrometers to work in an inert

atmosphere, vacuum, or no atmosphere at all, because matter

such as carbon dioxide and water vapor will absorb IR
radiation.13 This issue can be avoided by filling the spectrometer

with a noble gas during testing, by creating a vacuum inside, by

leaving no open areas between the source and the detector, or by

running a background scan to be subtracted from the sample

scan. The latter approach accounts for any of the erroneous

absorption as the beam passes through the sample chamber and

filter media, and was therefore employed in this study.

The FTIR spectrometer (Bruker Alpha) and the VFA spectrometer (Wilks InfraSpec) chosen for this study were both

designed for field portability and have the potential sensitivity to

quantify small amounts of silica deposited on filters. The VFA

has a fixed resolution of 25 cm-1 while the FTIR can be adjusted

by setting the mirror speed. For this study, the FTIR resolution

was set to 4 cm-1

, which has been shown previously to eliminate

unnecessary detail and thereby reduce noise16 while still

providing adequate peak identification and maintaining

a reasonably quick sampling time.

Both IR instruments are controlled by software that automatically generates an absorbance spectrum and sends it to the

interface screen for viewing, post-processing, and archiving.

Most modern spectrometers incorporate such software, designed

to identify and quantify the relative amplitude of absorbance

peaks. The software typically incorporates algorithms for baseline correction and peak integration, as well as routines for

deconvolution of peak overlaps. Additionally, some software is

capable of complex fitting schemes involving analysis of multiple

spectra, using a variety of mathematical models and reference to

stored libraries in order to discern commonalities of the spectra,

which it then correlates with known information (mass) for the

measurement of interest. For this work, while the manufacturer’s

software was used to integrate peak areas, additional libraries or

fitting algorithms were not employed.

Both IR instruments in this study were calibrated using filter

samples with known masses of a standard crystalline silica

reference material. After calibration, a kaolin correction scheme

was developed by analyzing multiple samples of well-characterized kaolin dust. Note that this laboratory-based method

requires the deposition of known amounts of well-characterized

material onto filters and using those samples to calibrate the

method. Such empirical calibration has been shown to be

repeatable if conducted carefully.1

Materials and methods

Materials used

The four types of coal dust used to evaluate the spectrometers—

Keystone Black, Pittsburgh 4, Pittsburgh 20, and Illinois 6—

were characterized previously.17 Keystone Black is a commercially available pre-ground coal dust with a median particle

diameter of 8 microns. Pittsburgh 4 and 20 are both derived from

the Pittsburgh coal seam and were ground to target mean

diameters of 4 and 20 microns, respectively. The coal dust from

the Illinois 6 seam was the finest dust used in this study, with

a target mean diameter of 3 microns.17

The Minusil reference material chosen for this study has

a purity of 89%.18 The material chosen for developing the kaolin

correction scheme was a well-characterized source of Georgia

kaolin, similar to that chosen in previous research where the

consistency of IR signals for various types of kaolin was

investigated.19

Filter sample preparation

Filter samples with known amounts of coal dust, silica, and

kaolin were made by aerosolizing each of these materials at

ambient conditions (25 °C, 50% Rel. Humidity) in a MarpleTM

Aerosol chamber, using a TSI 3400A Fluidized Bed Aerosol

Generator (TSI, Inc.). Samples were collected onto low ash,

37mm, 5.0 micron pore size, polyvinylchloride filters (SKC®

Corp., Inc.) that were pre-weighed by a gravimetric balance and

placed in plastic cassettes. Each sample was preceded by a DorrOliver cyclone. The samples were mounted in groups of six to ten

samples, with each sampler connected to a critical orifice calibrated to 1.7 Lpm and each group connected to a flow manifold.

A maximum of five manifolds were used per test run. The

chamber concentration was monitored using two series 1400

TEOM (tapered element oscillating microbalance) particulate

monitors (Thermo Scientific Model TEOM 1400ab Ambient

Particulate Monitor) connected to Dorr-Oliver cyclones and

operated at a flow rate of 1.7 Lpm. After collection, all filter

samples were post-weighed to determine the mass of dust loaded

onto each. For each group of filter samples collected during one

run, three unused filters from the same lot were set aside as

controls.

Sample analysis

The reference method used for comparison of the two IR

instruments was the MSHA P7 method. This entails ashing the

filters in a low-temperature radio-frequency asher to destroy any

organics including coal dust and the PVC filter itself. Ashed

samples are redeposited in a 9-mm-diameter circular area on

a vinyl acrylic copolymer filter (VAC-DM450). The redeposited,

ashed samples are then scanned by FTIR spectrometry between

frequencies of 1,000 and 700 cm-1 to determine the quartz and

kaolin content. When the absorbance spectra data are graphed,

silica appears as a set of doublet peaks at 780 and 800 cm-1 as

depicted in Fig. 1 for a pure quartz sample.

Fig. 1 Idealized IR spectra for silica and kaolin showing locations of

absorption peaks to highlight the region of overlap that interferes with

silica quantification.

In contrast, kaolin

presents a spectrum with a large peak at 915 cm-1 as well as

a smaller peak at 790 cm-1 that is in the same range as the silica

doublet peaks (Fig. 1). Therefore, when analyzing coal samples,

the silica doublet around 800 cm-1 cannot simply be analyzed

directly to determine the amount of silica because the peaks from
kaolin and quartz will interfere (combine) and indicate a larger

amount of free silica than is actually present.

Since the larger kaolin peak at 915 cm-1 is easily quantified on

coal samples, while the smaller peak is ‘‘masked’’ by the silica

present in the coal, MSHA developed a kaolin correction scheme

that entails analyzing the kaolin peaks at 915 cm-1 and 790 cm-1

for a standard kaolin material and using a peak ratio calibration

factor to estimate the size of the masked kaolin peak.8 This value

is then subtracted from the doublet peak at 800 cm-1

, and the

value of free silica in the sample is estimated.

The method used for analyzing filter samples with the portable

IR instruments entailed mounting the filter in a stainless steel

holder that was placed so that the filter was centered between the

IR source and the detector. The IR beam was then 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. Silica presented doublet peaks between 816 cm-1

and 767 cm-1 for FTIR and between 833 cm-1 and 745 cm-1 for

VFA. Kaolin presented a main peak between 930 cm-1 and 890

cm-1 for FTIR and between 952 cm-1 and 857 cm-1 for VFA.

Therefore, those integration regions were consistently used for all

testing.

Prior to sampling the silica or coal filters, 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 manufacturers’ software, and was necessary to

accurately quantify the silica peak. After the subtraction is performed, the remaining spectra relates only to the silica or coal

that was on the filter.

Instrument calibration using standard silica material

To discern the sensitivity and accuracy of the two IR instruments

for analyzing silica, both were first calibrated using samples of

Minusil dust. As a guide to the selection of appropriate sample

loadings, the NIOSH guidelines for air sampling and analytical

method development and evaluation were followed, which calls

for evaluating any new method in the range of 10–200% of the

permissible exposure limit in question.20 Using the current

regulation for silica, which calls for less than 5% silica on a 2 mg

sample, this translates into an evaluation range between 10 and

200 mg/filter. After samples were collected, one set was sent

directly to MSHA for P7 analysis while a second (parallel) set

was analyzed using the IR instruments. Calibration was based on

comparing the IR generated spectra, more specifically the areas

of the doublet absorbance peaks for silica, with the P7-derived

mass of the parallel silica samples.

Kaolin correction scheme

In the MSHA P7 method, correction for kaolin is accomplished

by running a set of kaolin standards of different loadings and

calculating the average area ratio of the 915 cm-1 and 790 cm-1

peaks (Fig. 1). It has been shown that this number is fairly

constant for each experimental setup using standard kaolin

material.19 Using that approach, i.e. analyzing multiple samples

of pure Georgia kaolin and taking the average ratio of the peak

areas, the kaolin correction ratios for the FTIR and VFA

instruments were found to be 3.8 and 12.4, respectively (Fig. 2).

In analyzing all coal dust samples, those ratios were used to

estimate the masked kaolin contribution, in order to subtract it

from the silica peak. This was done by using the following

equation:

- Coal area at 915 cm 1

Coal area - at 790 cm 1 ­ Kaolin correction ratio

1⁄4 Silica area at - 790 cm 1

(1)

It is notable that this kaolin correction ratio for VFA varies

with kaolin loading on the filters (Fig. 2).

Fig. 2 Ratios between the two main absorbance peaks for kaolin at 915

cm-1 and 790 cm-1 vs. sample loading, with horizontal lines representing

the average ratio for each instrument.

This outcome is related

to the spectral resolution and its effect on the accuracy of peak

integrations as will be discussed further in the Results and

Discussion section. To verify our choice of a constant value for

the correction ratio, we processed data using both the constant

value (12.4) and a linear fit to the data of Fig. 2 and found that

the effect on the resulting calibration curve was negligible.

Quantification of silica in coal dust

Once the silica calibration and kaolin correction were finalized,

the IR instruments were used to quantify the silica content of

challenge filters containing laboratory-generated coal dust,

loaded in the range of 100–2000 mg/filter. The method used to

derive quantitative values from the IR spectra were similar to the

approach described in the P7 method.8 This entailed measuring

the area of the silica absorbance doublet peak for the coal

samples, correcting it for kaolin by subtracting the estimated

kaolin contribution, and then using the silica calibration equation to derive silica mass. This process was used to measure silica

on four different types of well-characterized coal dust and the

results used to compare the performance of the two IR

instruments.

The number of samples collected and analyzed varied slightly

for the different cases. For each coal type, 12 total samples were

analyzed with three samples at each of four target loadings. The

three samples at each target loading were averaged for a total of

four data points. For Minusil, the same pattern was followed,

except there were 15 total samples and five target loadings.

Because VFA was not sensitive enough to give a clear reading for

the lowest Minusil samples, only 12 total samples at four target

loadings are presented in that data set.
Each group or subset of data (measurements made on similarly

loaded filters), were analyzed as follows, using guidelines developed by NIOSH.21 The data were averaged to give one value for

each of at least three parallel samples and a standard deviation

was calculated. The averaged values from all the data subsets

were plotted using Microsoft Excel, with the P7 data on the xaxis and the corresponding data for the IR instruments on the yaxis. A linear regression curve was calculated in Excel for each set

of data, with the equation for that curve describing the correlation between the averaged data and the P7 (reference) method.

For the Minusil samples, this correlation was subsequently used

as a calibration equation to calculate the estimated silica mass

from the raw IR data. The estimated masses were then plotted

against the reference masses provided by P7 analysis in order to

assess the prediction ability of each IR method. The quality of

correlation is reflected in the linear regression equation that was

calculated for the plotted data, i.e. by how close the slope is to 1

and by the magnitude of the bias (constant) term in the equation.

Results and discussion

Instrument calibration using Minusil

For both instruments, the total area of the doublet silica peaks in

the absorbance spectra were found to be linearly correlated with

mass of the parallel P7 samples (Fig. 3).

Fig. 3 Graph of P7-measured silica mass versus area of the silica doublet

peaks for FTIR and VFA instruments.

It is notable that the

sample-to-sample measurements vary more at each loading for

the VFA instrument, and that the variations result in a greater

bias compared to the FTIR instrument. The vertical SD bars

represent the variation in the IR data from triplicate filter

samples at each level of silica loading, while the horizontal bars

reflect the SDs of the parallel triplicate samples that were

analyzed by the P7 method. While these SDs include potential

errors due to sampling inaccuracies, such errors are assumed to

be identical for both datasets. The relative variations in the SDs

presented for the two methods therefore represent the differences

in the performance and repeatability of the two instruments. One

possible exception to that assumption is that there is the potential

for non-uniform deposition of dust across the filter face.22 This

can be addressed by taking multiple scans across the filter or by

placing the sample at a location where the beam is diverged (to

37 mm). The former was not deemed practical for these instruments while the latter reduces instrument analytical sensitivity by

an order of magnitude16 and therefore was not considered for

these tests.

Applying the calibration equations of Fig. 3 to IR data for

a separate set of five unknown Minusil samples and plotting

results against P7 data demonstrates the expected correlation

between P7 and IR measurements for pure silica (Fig. 4).

Fig. 4 Method correlation for Minusil: P7 measured mass versus IR

estimated mass for the FTIR and VFA instruments. The dashed diagonal

line indicates a slope of one.

This

correlation is the first step toward using the IR instruments for

measuring silica on coal samples, and suggests that, barring

interferences, either instrument could give reasonable estimates

of silica mass. Note that the lowest loading of Minusil (10 mg) is

missing from both the calibration and comparison graphs for

VFA, since that instrument did not produce consistent or accurate results for the low loading condition. Also, Fig. 4 does not

include error bars because only one filter sample was analyzed at

each loading, i.e. to evaluate the calibration equations.

Estimation of (kaolin corrected) silica mass on coal samples

The FTIR and VFA instruments were next used to analyze four

types of laboratory-generated coal samples for which the mass of

coal had been measured gravimetrically and silica mass determined by P7 analysis, using sets of parallel samples. The areas of

the kaolin-corrected silica peaks were used along with the calibration curves for each instrument (Fig. 3) to obtain estimated

silica mass. That estimated mass was then compared to P7­

derived silica mass of the parallel samples (Table 1). The

Keystone Black coal dust samples contained only about 0.5%

silica and, as expected, neither instrument was capable of

quantifying the small mass of silica in the samples. Those data

are therefore not included in Table 1 or in the discussion of the

results. As expected, the P7 correlations for the other three coals

were better for the FTIR instrument due to its better resolution

compared to the VFA instrument (4 cm-1 versus 25 cm-1

).

Smaller standard deviations in the sample-to-sample measurements of similarly loaded filters were observed for the FTIR

instrument as well as a smaller percent difference, as shown in

Table 1.

FTIR performance/results

The FTIR-estimated silica values for the three types of coal

samples correlate linearly with the measurement of silica on
parallel samples by the P7 method (Fig. 5), with slope very near

unity (dashed diagonal line in the figure). There is an apparent

lack of a trend in the sample-to-sample SD values, which

derives mainly from inconsistencies in the collection of material

onto the filters, as reflected in the rather high SDs for the

triplicate samples analyzed by the P7 method.

Table 1 Average results for each of the three coal types and the % difference between FTIR or VFA and P7

FTIR VFA

Avg Loading,

mg/filter Avg P7, mg/filter Avg Est Si, mg/filter

%

Difference

Avg Est Si,

mg/filter

%

Difference

P4 Coal 1

P4 Coal 2

P4 Coal 3

P4 Coal 4

Ill6 Coal 1

Ill6 Coal 2

Ill6 Coal 3

Ill6 Coal 4

P20 Coal 1

P20 Coal 2

P20 Coal 3

P20 Coal 4

107

618

1270

1910

258

613

1150

1750

219

508

1040

2040

4.3

35

67

94

11

26

50

64

14

33

63

120

9.2

31.5

57.7

94.9

14.2

26.9

46.9

62.9

16.1

32.3

62.9

119

110

10

14

1.0

29

3.5

6.2

1.7

15

2.1

0.16

0.83

42

61.1

101

88.7

80.4

92.7

124

124

54.7

45.2

71.5

107

880

75

50.7

5.6

630

260

150

94

290

37

13

11

Fig. 5 Correlation of P7 data and FTIR measurements of silica mass for

three types of coal dust samples. The dashed diagonal line indicates

a slope of one.

Fig. 6 Correlation of P7 data and VFA measurements of silica mass for

three types of coal dust samples. The dashed diagonal line indicates

a slope of one.
VFA performance/results

The VFA data for the coal samples does not correlate with the P7

method as well as the FTIR data (Fig. 6), and in particular the

sample-to-sample SD values for the VFA data are higher. The

higher SDs and poor correlation are not unexpected, since the

lower resolution of this instrument affects the consistency of

peak integrations as was reported previously23 and is discussed in

the next section of this paper. This in turn potentially affects the

accuracy of the kaolin correction and that result is more

pronounced at lower mass loadings, possibly contributing to the

very high bias for this method.

The wide variation in slope and bias for the three coals

suggests that VFA is much less accurate at measuring silica on

coal filters as opposed to pure silica samples. The deviations of

the slopes from unity further support that deduction. These

inconsistencies in the datasets led to further investigations of the

role of instrument resolution and its effect on the peak

quantification.

Since the FTIR instrument has the capability of resolution

adjustment when the mirror positioning speed is changed, the

approach was to re-run the Illinois 6 samples using the FTIR

instrumentset to a lowerresolution and compare the resultsto the

VFA data. For this investigation, the FTIR resolution was set to

25 cm-1

, identical to the VFA resolution. A new silica calibration

curve and a new kaolin correction were then generated using the

25 cm-1 setting and the Illinois 6 coal samples re-analyzed. It was

observed that lowering the resolution changes the resulting

spectra in two important ways that affect the accuracy of silica

quantification. The first is that the coarseness of the resolution

causes the silica doublet peak to morph into one larger peak.

Comparing the doublet peak at 800 cm-1 from the FTIR running

at resolution of 25 cm-1 with that for VFA (at fixed resolution of

25 cm-1

) showed that in both cases the doublet disappeared and

became one peak, resulting in potential for inaccurate representation of that region during peak integration calculations by the

software. A similarresult has been described previously23 and was

shown to result in small or narrow peaks being masked or

unrepresented due to dominance of larger neighboring peaks. The

problem is compounded in our case by varying the massloading of

the filters, as the lower loadings tend to have slightly shorter and

narrower spectral peaks while greater loadings have slightly taller

and wider peaks. Due to this subtle change, an instrument with

low resolution will not present consistently proportional spectral

peaks over the range of mass loadings.

The second effect caused by lower resolution is the relative

shifting of some peaksfor certain samples, introducing variability

into the peak-quantification process. This occurs when the

absorbance from multiple neighboring peaks gets combined due

to the coarseness of the resolution. For our case this is especially

important in performing accurate and repeatable kaolin correction using the peak centered at 915 cm-1 (Fig. 7).

Fig. 7 FTIR data from a single coal sample, showing the effect of

decreasing instrument resolution on the shape and position of the kaolin

peak at 915 cm-1

.

The data of that

figure show that as resolution decreases, the relative position and

magnitude of the original peaks affects the location of the

resulting single peak. Furthermore, the variability in interferents

among the different coals causessuch peak shiftsto be differentfor

each coal type. This hinders the consistency of peak area integrations when using a consistent analytical method for multiple

samples, which is necessary for a robust and dependable method.

For the above reasons, when the Illinois 6 samples were

analyzed by FTIR at resolution of 25 cm-1

, the results were also

unacceptable, including a large bias and a slope much greater

than unity (1.8). A plausible explanation for the steep slope is

that when peak shifting occurs, as it does for the larger kaolin

peak in the low resolution spectra of the Illinois 6 samples, the

kaolin correction is no longer valid, since the correction factor is

only applicable when the kaolin peak presents in the same region

in both the pure kaolin samples and the coal samples. Because

this issue does not arise for the FTIR analysis of Illinois 6

samples run at 4 cm-1 resolution (Fig. 5), and because the only

difference between the two FTIR results is resolution, the erroneous pattern across the board for VFA data appears to be

mainly a result of the lower resolution of that instrument. The

lack of resolution has the apparent effect of amplifying the

interaction with interferents, resulting in inaccuracies in the data.

It is clear that reducing the resolution of the IR method has

a significant effect on the quality of the results. Other factors that

may affect the results include the possibility that some larger

particles will have penetrated the cyclone24 and deposited on the

filter, potentially leading to a slight particle size dependence in

the IR method as reported previously.25 Additionally, an issue

with this VFA instrument that is not related to resolution is the

restriction to a certain band of IR radiation based on the LVF

used. While the FTIR instrument uses wavelengths from 400

cm-1 to 4000 cm-1

, the VFA instrument has a much narrower

band (740 cm-1

–1260 cm-1). The silica peak at 800 cm-1 is

therefore very close to the range limit of the LVF, where the LVF

and detector array are not optimized and thus susceptible to

higher noise levels.15 It is therefore possible that improved

accuracy could be gained from an LVF with range centered on

the peaks of interest.

The Keystone Black coal dust samples contained only about

0.5% silica and, as expected, neither instrument was capable of

quantifying the small mass of silica in the samples. The data were

therefore not included in the above discussion, but did serve to
confirm that the kaolin-corrected peak areas were not influenced

by the mass of coal on the filters, i.e. it is possible that the

increasing spectral peak areas could correlate in part to the

increasing mass of coal rather than (or in addition to) the mass of

silica. That possibility was disproved for this case by observing

the FTIR data from the Keystone Black coal samples, which

have silica content approximately ten times lower than the other

coals presented in this study. When the samples were analyzed

using FTIR and kaolin corrected, the estimated amount of silica

was approximately zero, which was consistent with the P7 data,

while the coal mass loadings from filter to filter increased

significantly, similar to the other samples. This confirmed that

the peak area of the 800 cm-1 doublet, after a kaolin correction,

actually represents the amount of silica on the filter. If the

correlation were linked to coal mass, the data would show falsely

heightened values for the estimated silica, with a linear increase

following the mass of coal on the filter. This was not the case,

however, so it was concluded that the FTIR analysis of the 800

cm-1 doublet, after a kaolin correction, represented only silica.

Conclusions

In summary, this study has provided data that demonstrate the

efficacy of using field-portable IR spectrometry methods for

measuring the mass of silica on filter samples of coal dust. The

following findings are relevant:

• Both IR instruments evaluated are potentially usable for

analysis of pure silica on filter media and yield linear correlations

between estimated silica mass and actual silica mass, at the

regulated respirable limit (100 mg).

• For quantifying silica on coal dust samples using a kaolincorrection scheme, the higher resolution of the FTIR instrument

shows much promise, even when silica content is relatively low.

• For the two instruments evaluated, spectral resolution was

the key factor affecting the difference in their ability to accurately

quantify silica in coal dust samples.

The next step is to demonstrate the feasibility of using such

a portable IR spectrometer to gather EOS data on a daily basis to

estimate the exposure of miners to airborne silica. Such testing

must also take into account other sources of error including

those due to sample collection and handling, analytical errors

due to sample overloading, and potential confounders including

kaolin and possibly other silica-bearing minerals. An effective

demonstration of an EOS method for quantifying silica on filter

samples of coal dust will enable mine operators to obtain daily

feedback about workplace silica levels. This knowledge will allow

more timely modifications to equipment or processes by implementation of engineering or administrative controls to reduce

worker exposure to silica and thus reduce silicosis in miners.

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 NIOSH.

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