Low level exposure to organophosphate (OP) pesticides can be determined by the measurement of dialkylphosphate (DAP) metabolites in urine. An analytical method is presented here which can measure the metabolites dimethyl phosphate (DMP), diethyl phosphate (DEP), dimethyl thiophosphate (DMTP), dimethyl dithiophosphate (DMDTP), diethyl thiophosphate (DETP), and diethyl dithiophosphate (DEDTP) at low levels. This was achieved by lyophilization of the urine, derivatization with pentafluorobenzyl bromide (PFBBr) and quantification by negative ion chemical ionization GC/MS-MS. The detection limits for the metabolites were 0.5 microg L(-1) DMP, 0.1 microg L(-1) DEP, 0.1 microg L(-1) DMTP, 0.04 microg L(-1) DMDTP, 0.04 microg L(-1) DETP and 0.02 microg L(-1) DEDTP. The RSD for the analytical method was 4-14% for the six metabolites. The method was used to monitor a group of non-occupationally exposed individuals in Sydney, Australia. The metabolites DMP, DEP, DMTP, DMDTP, DETP and DEDTP occurred in 73, 77, 96, 48, 100 and 2% of the samples with median values of 13, 3, 12, <1, 1 and 1 microg L(-1) respectively. The method is simple to use, sensitive and suitable for routine analysis of non-occupational exposure levels. These detection limits are between one and two orders of magnitude lower than those previously reported in the literature.

A routine gas chromatographic (GC) method is described for the analysis of dialkylphosphate metabolites in the urine of workers occupationally exposed to organophosphorus insecticides. The procedure involves derivatizing a freeze-dried urine sample with pentafluorobenzyl bromide and then determining the metabolites using dual capillary column GC with flame photometric detection.

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.

Welding is a hazardous process with an associated risk of health effects related to the fume arising from the core metal, flux components and welding surface. X-ray fluorescence (XRF) is commonly used to determine elemental concentrations as part of occupational hygiene investigations using conventional calibrations. A method is proposed to determine elements in welding fume using XRF and a fundamental parameter software package known as UniQuant  . This was found to remove the need for special dust standards being prepared as the calibration used was based on a series of standards supplied with UniQuant that would cover all sample types. A conventional calibration and UniQuant calibration were set up and elements found in welding fume were determined from tin to titanium. Samples obtained from the Health and Safety Laboratory Workplace Analysis Scheme for Proficiency (WASP) programme were also analysed by both methods for nickel, iron, manganese and chromium. For the normal calibration, average recovery results for the WASP samples were between 92 and 103% of the target value with relative standard deviations of 3-7%. For the UniQuant calibration, average recovery results for the WASP samples were between 97 and 112% of the target value with relative standard deviations of 3-10%. These results are well within analytical performance expectations for the type of welding fume matrix analysed. The method was applied to real welding fume samples collected from workplaces.

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