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.

Exposure to 4,4'-methylene bis (2-chloroaniline) (MbOCA) in New South Wales, Australia.

OBJECTIVES: This study was conducted to determine the level of exposure of 4,4'-methylene bis (2-chloroaniline) (MbOCA) in New South Wales (NSW), Australia. METHODS: An integrated occupational hygiene and biological monitoring program were used to assess the workers' exposure to MbOCA via inhalation, ingestion and dermal contact. This was conducted by personal air monitoring, static air monitoring and surface contamination monitoring of the work environment and biological monitoring of the workers' exposure to MbOCA at nine workplaces in NSW. RESULTS: The air monitoring results for MbOCA gave a geometric mean (GM) of 0.06 µg/m3 and a geometric standard deviation (GSD) of 2.70 and a 95% confidence interval of 0.29 µg/m3. The surface contamination in the main work area showed the highest contamination with a GM of 74 ng/cm2 and a GSD of 17 and a 95% confidence interval of 7,751 ng/cm2. Biological monitoring showed a GM of 0.89 µmol/mol cr and a GSD of 11.9 and a 95% confidence interval of 52 µmol/mol cr. This indicated that 13% of the workers were over the SafeWork NSW Biological Occupational Exposure Limit of 15 µmol/mol cr. CONCLUSIONS: Workers' exposure through inhalation was minimal; however, evidence from biological monitoring of MbOCA suggested that the main contributing factor to exposure was skin absorption. This was attributed to poor housekeeping and inadequate personal protection. Improvements in these areas were recommended, and it was also recommended to improve the awareness of the workers to the adverse effects to their health of exposure to this carcinogen.

A study of the conditions of measurement required to evaluate bias in analytical results illustrated by the use of data from a multi-round, blind-duplicated, proficiency test.

Measurement uncertainty estimated under repeatability conditions in batch chemical analysis using calibrated instruments may be considered to be composed of contributions from two major effects: (i) precision of the analysis that encompasses sufficient variability of the measurement and (ii) the assessment of trueness by quantifying and if necessary correcting for bias. This paper considers under what conditions of measurement to assess bias, and from the results of a six-round blind-duplicated interlaboratory proficiency program for creatinine in urine shows that bias is present in each individual run with components from that batch and from the laboratory over the rounds of the program. We conclude that bias should be determined in each batch run under repeatability conditions. Measurement of laboratory bias alone is not sufficient to account for effects in each batch run.

The determination of occupational exposure to polycyclic aromatic hydrocarbons by the analysis of 1-hydroxypyrene in urine using a simple automated online column switching device and high-performance liquid chromatography.

An analytical method using a liquid chromatograph combined with a simple online column switching sample pre-treatment system was developed for the determination of 1-hydroxypyrene (1-HP) in urine. This compound is the metabolite of pyrene and is used to assess the exposure of workers to polycyclic aromatic hydrocarbons (PAHs). After enzymatic hydrolysis, a urine sample was directly injected into a high-performance liquid chromatograph (HPLC) where it automatically underwent a sample cleanup using a column switching device. The procedure is simpler than previous methods because it uses only one switching valve, one extraction column and one HPLC pump. The analyte was retained on a short extraction column and after interferences were eluted to waste, was subsequently switched onto the analytical column. This allowed a short analysis time of 15 min. The calibration graph was found to be linear within the concentration range of 0.5 to 20 µg/L with a coefficient of determination exceeding r(2) = 0.99. Recoveries were found to be greater than 96% in the range 1 to 10 µg/L with intermediate precision of 2.5 to 5.8% relative standard deviation. This online method was verified by a comparison with an existing manual method by the analysis of 81 urine samples from workers exposed to PAHs and showed that the test results from both methods were in agreement with a probability obtained from the paired Student's t-test of P > 0.76. The proposed online method was found to be simple, fast and suited to routine analyses of 1-HP in urine for the assessment of occupational exposure to PAHs.

Treatment of bias in estimating measurement uncertainty.

Bias in an analytical measurement should be estimated and corrected for, but this is not always done. As an alternative to correction, there are a number of methods that increase the expanded uncertainty to take account of bias. All sensible combinations of correcting or enlarging uncertainty for bias, whether considered significant or not, were modeled by a Latin hypercube simulation of 125,000 iterations for a range of bias values. The fraction of results for which the result and its expanded uncertainty contained the true value of a simulated test measure and was used to assess the different methods. The strategy of estimating the bias and always correcting is consistently the best throughout the range of biases. For expansion of the uncertainty when the bias is considered significant is best done by SUMU(Max):U(C(test result))=ku(c)(C(test result))+ |delta(run)|, where k is the coverage factor (= 2 for 95% confidence interval), u(c) is the combined standard uncertainty of the measurement and delta(run) is the run bias.