Consideration of kaolinite interference correction for quartz measurements in coal mine dust

End-of-Shift Monitoring of Respirable Crystalline Silica: A Critical Review of Measurement Techniques and Factors Influencing Accurate Measurements

This article presents the findings of a systematic literature review focusing on the impact of interferences in host materials on Respirable Crystalline Silica (RCS) measurement, and the development of a validated method of calibration for RCS instrumentation for in-field use. The impact of interferences in host materials on RCS measurement has been shown to be critical information required for in-field use of portable RCS devices. Portable RCS instruments such as FTIR and XRD have been established to have good sensitivity for measurement but relatively low specificity due to interference in host materials. It has been shown that correction factors are needed to be applied to account for known interferences, especially when calibrating portable RCS instrumentation in-field. The potential to obtain reliable and repeatable results from RCS analysis instrumentation has also been demonstrated. There was significant evidence that interference materials could be present in host materials, and they influence RCS measurement. There is the potential and possibility of using portable RCS instrument for in-field measurement however the challenge is the calibration of such instrumentation in the field to account for changing host materials and thus potential interferences.

Comparison of the Analysis of Respirable Crystalline Silica in Workplace Air by Direct-on-Filter Methods using X-ray Diffraction and Fourier Transform Infrared Spectroscopy

A comparison of the analysis of respirable crystalline silica direct-on-filter methods using X-ray diffraction (XRD) and Fourier transform infrared (FT-IR) spectroscopy was undertaken using 253 real workplace air samples from road construction and tunnelling, coal mining, and kitchen benchtop manufacturing in Australia. Using pure α-quartz standards, XRD and FT-IR direct-on-filter analyses produced identical test results, however, the real workplace samples showed a clear discrepancy between FT-IR and XRD results with on average a 9% positive bias of the FT-IR results. The cause of the positive bias was due to matrix interferences which was confirmed by using synthetic mixture air samples. Approximately a third of the data by direct-on-filter method using FT-IR was assessed to be invalid based on the peak height ratio criterion due to excessive interferences and weight overload limitations. The XRD method showed better results due to less interference from the common matrices. XRD could handle up to twice the sample loading and at higher loadings up to 7 mg when a correction was applied. It was also able to achieve a lower limit of detection of 2 µg filter⁻¹ when a slower scan condition was utilized.

Multicomponent Measurement of Respirable Quartz, Kaolinite and Coal Dust using Fourier Transform Infrared Spectroscopy (FTIR): A Comparison Between Partial Least Squares and Principal Component Regressions

Exposure to respirable crystalline silica (RCS) is potentially hazardous to the health of thousands of workers in Great Britain. Both X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy can be used to measure RCS to assess exposures. The current method outlined in the Health and Safety Executive's (HSE) Methods for the Determination of Hazardous Substances (MDHS) guidance series is 'MDHS 101 Crystalline silica in respirable airborne dust - Direct-on-filter analyses by infrared spectroscopy or x-ray'. This describes a procedure for the determination of time-weighted average concentrations of RCS either as quartz or cristobalite in airborne dust. FTIR is more commonly employed because it is less expensive, potentially portable and relatively easy to use. However, the FTIR analysis of RCS is affected by spectral interference from silicates. Chemometric techniques, known as Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR), are two computational processes that have the capability to remove spectral interference from FTIR spectra and correlate spectral features with constituent concentrations. These two common chemometric processes were tested on artificial mixtures of quartz and kaolinite in coal dust using the same commercially available software package. Calibration, validation and prediction samples were prepared by collecting aerosols of these dusts onto polyvinylchloride (PVC) filters using a Safety in Mines Personal Dust Sampler (SIMPEDS) respirable cyclone. PCR and PLSR analyses were compared when processing the same spectra. Good correlations between the target values, measured using XRD, were obtained for both the PCR and PLSR models e.g. 0.98-0.99 (quartz), 0.98-0.98 (kaolinite) and 0.96-0.97 (coal). The level of agreement between PCR and PLSR was within the 95% confidence value for each analyte. Slight differences observed between predicted PCR and PLSR values were due to the number of optimal principal components applied to each chemometric process. The presence of kaolinite in these samples caused an 18% overestimation of quartz, for the FTIR, when following MDHS 101 without a chemometric method. Chemometric methods are a useful approach to obtain interference-free results for the measurement of RCS from some workplace environments and to provide a multicomponent analysis to better characterise exposures of workers.

Artificial Neural Networks (ANNs) and Partial Least Squares (PLS) Regression in the Quantitative Analysis of Respirable Crystalline Silica by Fourier-Transform Infrared Spectroscopy (FTIR)

Respirable crystalline silica (RCS) overexposure can lead to the development of silicosis which is a chronic, irreversible, potentially fatal respiratory disease. The most significant prerequisite for any silica exposure control plan is an accurate occupational exposure assessment. The results of crystalline silica analysis are often affected by other mineral interferences and are influenced by an analyst's knowledge of mineralogy to accurately interpret infrared spectra and correct matrix interferences. Partial least squares (PLS) and artificial neural networks (ANNs) are two multivariate calibration methods to overcome the problem of spectral interferences without the need for an analyst intervention. The performance of these two methods in quantitative analysis of quartz in the presence of mineral interferences was evaluated and compared in this study. Fifty mixtures with different crystalline silica content ratios were prepared by mixing quartz with four common mineral interferences including kaolinite, albite, muscovite, and amorphous silica. Fourier-transform infrared spectra of the mixtures were split into training and test datasets. The optimal architecture of the ANN model was achieved using a two-level full factorial design experiment and data were modeled using ANN and PLS regression analysis. Root mean squared error of prediction values of 1.69 and 6.12 µg quartz for ANN and PLS models, respectively, revealed the fact that the both models performed very well in quantitative analysis of quartz in the presence of mineral interferences, with a better relative performance of the ANN model which can be related to the inherent nonlinear predictive ability of ANNs. Given the excellent predictive ability of the ANN model which can deal with a completely overlapped peak without any need of user's intervention, it is recommended that the ANN model be optimized in future studies and utilized for reliable and rapid on-field assessment of RCS exposure.

Promoting early exposure monitoring for respirable crystalline silica: Taking the laboratory to the mine site

The exposure to respirable crystalline silica (RCS) in the mining industry is a recognized occupational hazard. The assessment and monitoring of the exposure to RCS is limited by two main factors: (1) variability of the silica percent in the mining dust and (2) lengthy off-site laboratory analysis of collected samples. The monitoring of respirable dust via traditional or real-time techniques is not adequate. A solution for on-site quantification of RCS in dust samples is being investigated by the Office of Mine Safety and Health Research, a division of the National Institute for Occupational Safety and Health. The use of portable Fourier transform infrared analyzers in conjunction with a direct-on-filter analysis approach is proposed. The progress made so far, the necessary steps in progress, and the application of the monitoring solution to a small data set is presented. When developed, the solution will allow operators to estimate RCS immediately after sampling, resulting in timelier monitoring of RCS for self-assessment of compliance at the end of the shift, more effective engineering monitoring, and better evaluation of control technologies.