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Primary aromatic amines (PAAs) are a class of hazardous substances where many compounds are classified as carcinogen, mutagen, and reproduction toxin (CMR). PAAs can be taken up by dermal exposure. In the polyurethane industry, a valid and trustworthy method for the quantification of PAAs in the presence of isocyanates that could interfere is of great interest, especially on workplaces where a regular contact to PAAs cannot be excluded. The aim of this work is the development, validation, and verification of a novel sampling device to quantify selectively the PAA load on work surfaces. We describe the synthesis of Cell-ßALA-PEMSA analytical papers and their characterization by infrared spectroscopy and thermogravimetric analysis. The recovery of TDA and MDA spiked on these filters is satisfactory. An excellent selectivity of Cell-ßALA-PEMSA papers towards PAAs in the presence of isocyanates of almost 100% was found by wipe tests of amine/isocyanate contaminated surfaces. First positive field tests were achieved at certain areas in a Polyurethane Technical Application Department where surface contamination with PAAs was expected, and the Cell-ßALA-PEMSA analytical papers were superior to an established method of surface sampling. However, recovery of these amines from surfaces shows a large variability, and more work is required to address influencing surface properties. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]