A Method for Evaluating Exposure to Nitrous Oxides by Application of Lognormal Distribution
A Method for Evaluating Exposure to Nitrous Oxides by Application of Lognormal Distribution: S.S. Borjanovic, et al. Department of Work Physiology, Institute of Occupational Health “Dr Dragomir Karajovic”—A lognormal distribution adequately describes exposure to nitrous oxides in a work environment. The aim of this paper was to assign to the measured data a certain degree of variability which defines the interval in which real concentrations can be found with a given probability. Exposures may be evaluated by using estimates of the geometric mean (GM) and the geometric standard deviation (GSD), i.e. central value and dispersion index, to calculate the confidence interval (Cl) around the mean exposure and compare this interval to the occupational exposure limit. The concentration of nitrous oxides (114 random temporal measurements covering all three shifts, during 6 consecutive days) on coated electrode welding in a car manufacturing plant, was determined with colourimetric direct reading method. Statistical analysis (chi‐square and Kolmogorov‐Smirnov goodness of fit tests, lognormal and Gaussian distribution fitting and Q‐G plots) was performed. The distribution of the nitrous oxides concentration in the work environment studied closely resembled that of lognormal distribution. The geometric mean was 4.098 mg/m3, median 4.00 mg/m3, geometric standard deviation 1.829 and 95% confidence interval 3.66‐4.58 mg/m3. It is possible to apply the computed GSD for evaluation of exposure limits to nitrous oxides in any other work environment, even with only a few measurements.
CHARACTERIZING THE VARIABILITY IN RESPIRABLE DUST EXPOSURE USING JOHNSON TRANSFORMATION AND RE-EXAMINING 2010 PROPOSED CHANGES TO THE U.S. UNDERGROUND COAL MINE DUST STANDARD
of Thesis Coal workers pneumoconiosis (CWP), commonly referred to as black lung, is a chronic lung disease that results from the inhalation and deposition of coal dust in the lungs. While this disease continues to afflict coal miners, its prevalence has steadily declined over three decades since 1970. Based on a voluntary X-ray surveillance program, conducted by the National Institute for Occupational Safety and Health (NIOSH), this downward trend, however, ended in 2000 and has actually begun to rise. The Mine Safety and Health Administration (MSHA) instituted a Comprehensive Initiative to ”End Black Lung” to combat the reported upturn in black lung disease. Rulemaking, with the intent of strengthening respirable dust regulations, is a major part of this initiative. This thesis addresses a controversial aspect of the newly proposed rules single-shift compliance sampling. Establishing new requirements for respirable dust compliance requires an understanding of both the accuracy and variability of measurements. Measurement variability is especially important in underground mining where the workplace is constantly moving and ventilation controls are continually changing. The results of a ventilation study performed in three underground coal mines are presented in this thesis. A total of 600 dust-concentration measurements were obtained in this study using Continuous Personal Dust Monitors (CPDMs). The data was analyzed to determine the variability associated with taking dust measurements in the mining workplace. The Johnson transformation was found to produce the best-fit distribution model for the data. This thesis summarizes the results of this study and presents a statistical procedure for establishing an exposure limit.
A task-based statistical model of a worker's exposure distribution: Part I--Description of the model.
The authors present a task-based model to describe a single worker's exposures to a single airborne chemical toxicant. The model accounts for variability in short-term time-weighted average (TWA) exposure values within a task, and for variability in arithmetic mean exposure levels between tasks. For a given workday, the 8-hour TWA value is equated with the sample mean of an appropriate number of short-term TWAs arising from stratified random sampling of short-term TWAs with proportional allocation by task. The model accounts for autocorrelation in the stochastic process that generates successive short-term TWA values. Due to the underlying random process, a given type of workday with regard to the set of task times has an associated distribution of 8-hour TWA values; the variance of this distribution increases with increasing autocorrelation in the time series of short-term TWAs. A worker's total distribution of 8-hour TWAs is a mixture of these day-specific distributions weighted by the relative frequency of each type of workday; the variance of the total distribution increases with greater day-to-day variability in the array of task times.