The negative binomial distribution is adopted for analyzing asbestos fiber counts so as to account for both the sampling errors in capturing only a finite number of fibers and the inevitable human variation in identifying and counting sampled fibers. A simple approximation to this distribution is developed for the derivation of quantiles and approximate confidence limits. The success of the approximation depends critically on the use of Stirling's expansion to sufficient order, on exact normalization of the approximating distribution, on reasonable perturbation of quantities from the normal distribution, and on accurately approximating sums by inverse-trapezoidal integration. Accuracy of the approximation developed is checked through simulation and also by comparison to traditional approximate confidence intervals in the specific case that the negative binomial distribution approaches the Poisson distribution. The resulting statistics are shown to relate directly to early research into the accuracy of asbestos sampling and analysis. Uncertainty in estimating mean asbestos fiber concentrations given only a single count is derived. Decision limits ( limits of detection) and detection limits are considered for controlling false-positive and false-negative detection assertions and are compared to traditional limits computed assuming normal distributions.

A simulation study was conducted to provide a more thorough account of measurement error associated with interval sampling methods. A computer program simulated the application of momentary time sampling, partial-interval recording, and whole-interval recording methods on target events randomly distributed across an observation period. The simulation yielded measures of error for multiple combinations of observation period, interval duration, event duration, and cumulative event duration. The simulations were conducted up to 100 times to yield measures of error variability. Although the present simulation confirmed some previously reported characteristics of interval sampling methods, it also revealed many new findings that pertain to each method's inherent strengths and weaknesses. The analysis and resulting error tables can help guide the selection of the most appropriate sampling method for observation-based behavioral assessments.

The performance of three MIRAN SapphIRe Portable Infrared Ambient Air Analyzers and three Century Portable Toxic Vapor Analyzers equipped with photoionization (PID) and flame ionization (FID) detectors was compared with charcoal tube sampling. Relationships were investigated using two different calibration methods at four cyclohexane concentrations, three temperatures, and four relative humidities. For the first method, the TVA monitors were calibrated with a single concentration of methane for the FID, and isobutylene for the PID. The SapphIRe monitors were zeroed and the monitor's manufacturer-supplied library was used. For the second method, a five-point cyclohexane calibration curve was created for each monitor. Comparison of the monitor results of each calibration method (pooled data) indicated a significant difference between methods (t-test, p < 0.001), The SapphIRe group had results closer to the charcoal tubes with the second calibration method, while the PID and FID monitor groups performed better using the first calibration method. The PID monitor group's performance was affected only at the 90% relative humidity (RH) condition. Using the first method, the monitor readings were compared with the charcoal tube average using mixed linear model analyses of variance (ANOVAs) and regression. The ANOVA results showed there was a statistically significant difference among readings from all monitor types (p <0.0001). The regression results demonstrated that the SapphIRe (r2 = 0.97) and FID (r2 = 0.92) monitor groups correlated well with the charcoal tubes. The PID monitor group had a similar correlation when 90% RH was excluded (r2 = 0.94) but had a weaker correlation when it was included (r2 = 0.58). The operator should take care when using these monitors at high concentrations and the PID monitors at high humidities, consider the variability between units of the same monitor, and conduct performance verification of the monitor being used. [Supplementary materials are available for this article. Go to the publisher's online edition of the Journal of Occupational and Environmental Hygiene for the following free supplemental resources: a program listing, schematic diagrams, and other design details for the test automation system.]

The Control of Substances Hazardous to Health (COSHH) Essentials model was evaluated using full-shift exposure measurements of five chemical components in a mixture [acetone, ethylbenzene, methyl ethyl ketone, toluene, and xylenes] at a medium-sized plant producing paint materials. Two tasks, batch-making and bucket-washing, were examined. Varying levels of control were already established in both tasks and the average exposures of individual chemicals were considerably lower than the regulatory and advisory 8-h standards. The average exposure fractions using the additive mixture formula were also less than unity (batch-making: 0.25, bucket-washing: 0.56) indicating the mixture of chemicals did not exceed the combined occupational exposure limit (OEL). The paper version of the COSHH Essentials model was used to calculate a predicted exposure range (PER) for each chemical according to different levels of control. The estimated PERs of the tested chemicals for both tasks did not show consistent agreement with exposure measurements when the comparison was made for each control method and this is believed to be because of the considerably different volatilities of the chemicals. Given the combination of health hazard and exposure potential components, the COSHH Essentials model recommended a control approach ‘special advice’ for both tasks, based on the potential reproductive hazard ascribed to toluene. This would not have been the same conclusion if some other chemical had been substituted (for example styrene, which has the same threshold limit value as toluene). Nevertheless, it was special advice, which had led to the combination of hygienic procedures in place at this plant. The probability of the combined exposure fractions exceeding unity was 0.0002 for the batch-making task indicating that the employees performing this task were most likely well protected below the OELs. Although the employees involved in the bucket-washing task had greater potential to exceed the threshold limit value of the mixture (P > 1 = 0.2375), the expected personal exposure after adjusting for the assigned protection factor for the respirators in use would be considerably lower (P > 1 = 0.0161). Thus, our findings suggested that the COSHH essentials model worked reasonably well for the volatile organic chemicals at the plant. However, it was difficult to override the reproductive hazard even though it was meant to be possible in principle. Further, it became apparent that an input of existing controls, which is not possible in the web-based model, may have allowed the model be more widely applicable. The experience of using the web-based COSHH Essentials model generated some suggestions to provide a more user-friendly tool to the model users who do not have expertise in occupational hygiene.