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New two-box "well-mixed room" decreasing emission (DE) models are introduced for scenarios that involve local controls, such as some form of local exhaust or local exhaust with filtered return. In addition, these models allow for the recirculation of a filtered (or cleaned) portion of the general room ventilation. For each control device scenario, a steady state and transient near and far field model is presented. The transient equations predict the concentration at time t after the application of the substance. The steady state equations can be use to predict the steady, unvarying "average concentration per application" whenever there are continuous applications of a substance and sufficient time has elapsed. The steady state equations can also be used to calculate the TWA for a task (or a series of tasks) whenever the beginning and end concentrations for the task (or task series) are expected to be zero (or near zero). The transient equations should be used to predict TWA exposures whenever these criteria cannot be met, or it is necessary to predict short-term exposures or peak concentrations. A structured calibration procedure, based on a mass balance approach, is proposed for each model. Depending upon the model, one or more calibration measurements are collected. Rearranged versions of the steady state equations are used to calculate estimates of the mass applied during each application, the near field flowrate, and (depending upon the model) the various efficiencies (e.g., local exhaust capture efficiency and the recirculation filtration efficiency). The emission rate constant must be determined using either a published approximation algorithm or experimentally.

New one box "well-mixed room" decreasing emission (DE) models are introduced that allow for local exhaust or local exhaust with filtered return, as well the recirculation of a filtered (or cleaned) portion of the general room ventilation. For each control device scenario, a steady state and transient model is presented. The transient equations predict the concentration at any time t after the application of a known mass of a volatile substance to a surface, and can be used to predict the task exposure profile, the average task exposure, as well as peak and short-term exposures. The steady state equations can be used to predict the "average concentration per application" that is reached whenever the substance is repeatedly applied. Whenever the beginning and end concentrations are expected to be zero (or near zero) the steady state equations can also be used to predict the average concentration for a single task with multiple applications during the task, or even a series of such tasks. The transient equations should be used whenever these criteria cannot be met. A structured calibration procedure is proposed that utilizes a mass balance approach. Depending upon the DE model selected, one or more calibration measurements are collected. Using rearranged versions of the steady state equations, estimates of the model variables-e.g., the mass of the substance applied during each application, local exhaust capture efficiency, and the various cleaning or filtration efficiencies-can be calculated. A new procedure is proposed for estimating the emission rate constant.

In volume 14, issue 1 of Journal of Occupational and Environmental Hygiene, an incorrect equation is shown in “Models for Nearly Every Occasion: Part II - Two Box Models”, by Gary H. Ganser and Pau...

The standard "well mixed room," "one box" model cannot be used to predict occupational exposures whenever the scenario involves the use of local controls. New "constant emission" one box models are proposed that permit either local exhaust or local exhaust with filtered return, coupled with general room ventilation or the recirculation of a portion of the general room exhaust. New "two box" models are presented in Part II of this series. Both steady state and transient models were developed. The steady state equation for each model, including the standard one box steady state model, is augmented with an additional factor reflecting the fraction of time the substance was generated during each task. This addition allows the easy calculation of the average exposure for cyclic and irregular emission patterns, provided the starting and ending concentrations are zero or near zero, or the cumulative time across all tasks is long (e.g., several tasks to a full shift). The new models introduce additional variables, such as the efficiency of the local exhaust to immediately capture freshly generated contaminant and the filtration efficiency whenever filtered exhaust is returned to the workspace. Many of the model variables are knowable (e.g., room volume and ventilation rate). A structured procedure for calibrating a model to a work scenario is introduced that can be applied to both continuous and cyclic processes. The "calibration" procedure generates estimates of the generation rate and all of remaining unknown model variables.

The mortality of 2650 employees (93.4% males) in the mine and mill production of roofing granules at four plants was examined between 1945 and 2004. Hypotheses focused on diseases associated with exposure to silica: nonmalignant respiratory disease, lung cancer, and nonmalignant renal disease. Study eligibility required ≥ 1 year of employment by 2000. Work history and vital status were followed through 2004 with1% lost to follow-up. Industrial hygiene sampling data (1871 sampling measurements over a 32-year period) and professional judgment were used to construct 15 respirable crystalline silica exposure categories. A category was assigned to all plant-, department-, and time-dependent standard job titles. Cumulative respirable crystalline silica exposure (mg/m(3)-years) was calculated as the sum of the product of time spent and the average exposure for each plant-, department-, job-, and calendar-year combination. The cohort geometric mean was 0.17 mg/m(3)-years (geometric standard deviation 4.01) and differed by plant. Expected deaths were calculated using U.S. (entire cohort) and regional (each plant) mortality rates. Poisson regression was used for internal comparisons. For the entire cohort, 772 deaths (97.4% males) were identified (standardized mortality ratio 0.95, 95% CI 0.88-1.02). There were 50 deaths from nonmalignant respiratory diseases (1.14, 95% CI 0.85-1.51). Lagging exposure 15 years among the male cohort, the relative risks for nonmalignant respiratory disease were 1.00 (reference), 0.80, 1.94, and 2.03 (p value trend = 0.03) when cumulative exposure was categorized0.1, 0.1-0.5, 0.5-1.0, and ≥ 1.0 mg/m(3)-years, respectively. There was a total of 77 lung cancer deaths (1.11, 95% CI 0.88-1.39). Lagging exposure 15 years, the relative risks for males were 1.00 (reference), 1.83, 1.83, and 1.05 (p value trend = 0.9). There were 16 deaths from nonmalignant renal disease (1.76, 95% CI 1.01-2.86). This exposure-response trend was suggestive but imprecise. The study results are consistent with other cohorts with similar levels of exposure to respirable crystalline silica.

A study was conducted to construct a job exposure matrix for the roofing granule mine and mill workers at four U.S. plants. Each plant mined different minerals and had unique departments and jobs. The goal of the study was to generate accurate estimates of the mean exposure to respirable crystalline silica for each cell of the job exposure matrix, that is, every combination of plant, department, job, and year represented in the job histories of the study participants. The objectives of this study were to locate, identify, and collect information on all exposure measurements ever collected at each plant, statistically analyze the data to identify deficiencies in the database, identify and resolve questionable measurements, identify all important process and control changes for each plant-department-job combination, construct a time line for each plant-department combination indicating periods where the equipment and conditions were unchanged, and finally, construct a job exposure matrix. After evaluation, 1871 respirable crystalline silica measurements and estimates remained. The primary statistic of interest was the mean exposure for each job exposure matrix cell. The average exposure for each of the four plants was 0.042 mg/m(3) (Belle Mead, N.J.), 0.106 mg/m(3) (Corona, Calif.), 0.051 mg/m(3) (Little Rock, Ark.), and 0.152 mg/m(3) (Wausau, Wis.), suggesting that there may be substantial differences in the employee cumulative exposures. Using the database and the available plant information, the study team assigned an exposure category and mean exposure for every plant-department-job and time interval combination. Despite a fairly large database, the mean exposure for95% of the job exposure matrix cells, or specific plant-department-job-year combinations, were estimated by analogy to similar jobs in the plant for which sufficient data were available. This approach preserved plant specificity, hopefully improving the usefulness of the job exposure matrix.

Fluid sealing devices (gaskets and packing) containing asbestos are manufactured and blended with binders such that the asbestos fibers are locked in a matrix that limits the potential for fiber release. Occasionally, fluid sealing devices fail and need to be replaced or are removed during preventive maintenance activities. This is the first study known to pool over a decade's worth of exposure assessments involving fluid sealing devices used in a variety of applications. Twenty-one assessments of work activities and air monitoring were performed under conditions with no mechanical ventilation and work scenarios described as "worst-case" conditions. Frequently, the work was conducted using aggressive techniques, along with dry removal practices. Personal and area samples were collected and analyzed in accordance with the National Institute for Occupational Safety and Health Methods 7400 and 7402. A total of 782 samples were analyzed by phase contrast microscopy, and 499 samples were analyzed by transmission electron microscopy. The statistical data analysis focused on the overall data sets which were personal full-shift time-weighted average (TWA) exposures, personal 30-min exposures, and area full-shift TWA values. Each data set contains three estimates of exposure: (1) total fibers; (2) asbestos fibers only but substituting a value of 0.0035 f/cc for censored data; and (3) asbestos fibers only but substituting the limit of quantification value for censored data. Censored data in the various data sets ranged from 7% to just over 95%. Because all the data sets were censored, the geometric mean and geometric standard deviation were estimated using the maximum likelihood estimation method. Nonparametric, Kaplan-Meier, and lognormal statistics were applied and found to be consistent and reinforcing. All three sets of statistics suggest that the mean and median exposures were less than 25% of 0.1 f/cc 8-hr TWA sample or 1.0 f/cc 30-min samples, and that there is at least 95% confidence that the true 95th percentile exposures are less than 0.1 f/cc as an 8-hr TWA.

A model is presented for applying Bayesian statistical techniques to the problem of determining, from the usual limited number of exposure measurements, whether the exposure profile for a similar exposure group can be considered a Category 0, 1, 2, 3, or 4 exposure. The categories were adapted from the AIHA exposure category scheme and refer to (0) negligible or trivial exposure (i.e., the true X 0.95or =1%OEL), (1) highly controlled (i.e., X 0.95or =10%OEL), (2) well controlled (i.e., X 0.95or =50%OEL), (3) controlled (i.e., X 0.95or =100%OEL), or (4) poorly controlled (i.e., X0.95or =1%OEL) exposures. Unlike conventional statistical methods applied to exposure data, Bayesian statistical techniques can be adapted to explicitly take into account professional judgment or other sources of information. The analysis output consists of a distribution (i.e., set) of decision probabilities: e.g., 1%, 80%, 12%, 5%, and 2% probability that the exposure profile is a Category 0, 1, 2, 3, or 4 exposure. By inspection of these decision probabilities, rather than the often difficult to interpret point estimates (e.g., the sample 95th percentile exposure) and confidence intervals, a risk manager can be better positioned to arrive at an effective (i.e., correct) and efficient decision. Bayesian decision methods are based on the concepts of prior, likelihood, and posterior distributions of decision probabilities. The prior decision distribution represents what an industrial hygienist knows about this type of operation, using professional judgment; company, industry, or trade organization experience; historical or surrogate exposure data; or exposure modeling predictions. The likelihood decision distribution represents the decision probabilities based on an analysis of only the current data. The posterior decision distribution is derived by mathematically combining the functions underlying the prior and likelihood decision distributions, and represents the final decision probabilities. Advantages of Bayesian decision analysis include: (a) decision probabilities are easier to understand by risk managers and employees; (b) prior data, professional judgment, or modeling information can be objectively incorporated into the decision-making process; (c) decisions can be made with greater certainty; (d) the decision analysis can be constrained to a more realistic "parameter space" (i.e., the range of plausible values for the true geometric mean and geometric standard deviation); and (e) fewer measurements are necessary whenever the prior distribution is well defined and the process is fairly stable. Furthermore, Bayesian decision analysis provides an obvious feedback mechanism that can be used by an industrial hygienist to improve professional judgment. For example, if the likelihood decision distribution is inconsistent with the prior decision distribution then it is likely that either a significant process change has occurred or the industrial hygienist's initial judgment was incorrect. In either case, the industrial hygienist should readjust his judgment regarding this operation.

For more than 20 years CSX Transportation (CSXT) has collected exposure measurements from locomotive engineers and conductors who are potentially exposed to diesel emissions. The database included measurements for elemental and total carbon, polycyclic aromatic hydrocarbons, aromatics, aldehydes, carbon monoxide, and nitrogen dioxide. This database was statistically analyzed and summarized, and the resulting statistics and exposure profiles were compared to relevant occupational exposure limits (OELs) using both parametric and non-parametric descriptive and compliance statistics. Exposure ratings, using the American Industrial Health Association (AIHA®) exposure categorization scheme, were determined using both the compliance statistics and Bayesian Decision Analysis (BDA).The statistical analysis of the elemental carbon data (a marker for diesel particulate) strongly suggests that the majority of levels in the cabs of the lead locomotives (n = 156) were less than the California guideline of 0.020 mg/m3. The sample 95th percentile was roughly half the guideline; resulting in an AIHA exposure rating of category 2/3 (determined using BDA). The elemental carbon (EC) levels in the trailing locomotives tended to be greater than those in the lead locomotive; however, locomotive crews rarely ride in the trailing locomotive. Lead locomotive EC levels were similar to those reported by other investigators studying locomotive crew exposures and to levels measured in urban areas. Lastly, both the EC sample mean and 95%UCL were less than the Environmental Protection Agency (EPA) reference concentration of 0.005 mg/m3.With the exception of nitrogen dioxide, the overwhelming majority of the measurements for total carbon, polycyclic aromatic hydrocarbons, aromatics, aldehydes, and combustion gases in the cabs of CSXT locomotives were either non-detects or considerably less than the working OELs for the years represented in the database. When compared to the previous American Conference of Governmental Industrial Hygienists (ACGIH) threshold limit value (TLV®) of 3 ppm the nitrogen dioxide exposure profile merits an exposure rating of AIHA exposure category 1. However, using the newly adopted TLV of 0.2 ppm the exposure profile receives an exposure rating of category 4. Further evaluation is recommended to determine the current status of nitrogen dioxide exposures.[Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource: additional text on OELs, methods, results, and additional figures and tables.]

The idea that the American Conference of Governmental Industrial Hygienists (ACGIH) time-weighted average (TWA) threshold limit values (TLVs) and the Occupational Safety and Health Administration (OSHA) TWA permissible exposure limits (PELs) were and are intended to be used as upper control limits for each worker's long-term mean exposure (and not as upper control limits for single-shift TWA exposures) continues to be advanced in the industrial hygiene literature. The corollary concept that compliance with a TWA TLV or TWA PEL is best determined using mean testing is also advanced. The objective of this article is to examine two questions: (1) Do long-term occupational exposure limits (LTA OELs) exist? and (2) What issues should be considered when designing an exposure monitoring program for a true LTA OEL? These issues will be examined from the viewpoints of the employer, inspector, and employees. Assuming that a valid LTA OEL exists, there are numerous issues to be addressed. These include dete...

The purpose of this article is to compare six procedures for comparing the estimated mean of data drawn from a lognormal distribution to a long-term average occupational exposure limit (LTA OEL): the standard t-test, the American Industrial Hygiene Association's (AIHA's) mean test, the modified Cox mean test, Rappaport and Selvin's mean test, Lyles and Kupper's mean test, and Land's mean test (a procedure based on Land's exact confidence intervals). In principle, all of these procedures, with one exception, can be used as either an employer's test or an inspector's test. Computer simulation was used to determine (1) the actual confidence level for each procedure for the situation where exposures are lognormally distributed and the true mean equals an LTA OEL, and (2) the power of each procedure when the true mean is different from the LTA OEL. Land's mean test consistently provided confidence levels near the nominal confidence level for all sample sizes and geometric standard deviations (GSDs) ev...

The determination that a work environment is acceptable or unacceptable is often based upon the calculation of the mean exposure and/or exceedance fraction [i.e., the fraction of measurements expected to exceed an occupational exposure limit (OEL)] for an employee or exposure group. The purpose of this article is to introduce simplified procedures for calculating accurate estimates of the 95 percent lower confidence limit (LCL) and 95 percent upper confidence limit (UCL) for (1) the sample arithmetic mean and (2) the sample exceedance fraction for lognormally distributed exposure data. The procedure for the arithmetic mean is adapted from Land's procedure for calculating exact confidence intervals around the mean of lognormally distributed data. The procedure for the exceedance fraction is based on the Odeh and Owen confidence limits for a proportion in one tail of the normal distribution. Both procedures are graphically based and require no hard-to-find references. The two 95 percent confidence ...

Formulae are presented for calculating the approximate sample size needed to estimate the true arithmetic mean or true geometric mean exposure for an exposure group to within a specified accuracy (±x% of the true arithmetic or geometric mean) with a specified level of confidence. These formulae are intended for use in prospective or cross-sectional occupational health studies, or when building an exposure database for use in assessing long-term changes in worker health status. They are applicable where the investigator is satisfied that the distribution of exposures within a group can be approximated by a lognormal distribution. The formulae were validated by computer simulation and show that large sample sizes are required when the existing parameter estimates were derived from a limited number of prior measurements and/or the true exposure distribution has a large geometric standard deviation. When summed across all exposure groups, an unreasonable total sample size may result. The total sample burden c...

This study examines the impact of several experience and education determinants on exposure judgment accuracy. The study used desktop assessments performed on several different tasks with different exposure profiles to identify correlations between determinants and judgment accuracy using logistic regression models. The exposure judgments were elicited from industrial hygienists with varying levels of experience, education, and training. Videos and written and oral information about the exposure tasks were presented to all participants as they documented a series of qualitative and quantitative exposure judgment probabilities in four exposure categories. Participants (n = 77) first documented their qualitative and then their quantitative exposure assessments after receiving the series of sampling data points. Data interpretation tests and training in simple rules-of-thumb for data interpretation were also given to each participant to investigate the impact of data interpretation skills on exposure judgment accuracy. Logistic regression analysis indicated "years of exposure assessment experience" (p0.05), "highest EHS degree" (p0.05), and a participant's "data interpretation test score" (p0.05) directly impacted qualitative exposure judgment accuracy. Logistic regression models of quantitative judgment accuracy showed positive correlation with "greater than 10 years of exposure assessment experience" (p0.05), "highest EHS degree" (p0.05), a participant's "data interpretation test score" (p0.001), rules-of-thumb data interpretation training (p0.001), and the number of sample data points available for a judgment (p0.005). Analyzing judgments in subsets for participants with less or more than 10 years' experience indicated additional correlations with Certified Industrial Hygienist and Certified Safety Professional certifications, total number of task exposure assessments, and career number of air surveys. The correlation of qualitative and quantitative exposure judgment accuracy with "greater than 10 years experience" supports similar research findings from other fields. The results of this study indicate that several determinants of experience, education, and training, in addition to the availability of sampling data, significantly impact the accuracy of exposure assessments. The findings also suggest methods for enhancing exposure judgment accuracy through statistical tools, mathematical exposure modeling, and specific training.

When analyzing censored datasets, where one or more measurements are below the limit of detection (LOD), the maximum likelihood estimation (MLE) method is often considered the gold standard for estimating the GM and GSD of the underlying exposure profile. A new and relatively simple substitution method, called beta-substitution, is presented and compared with the MLE method and the common substitution methods (LOD/2 and LOD/square root(2) substitution) when analyzing a left-censored dataset with either single or multiple censoring points. A computer program was used to generate censored exposure datasets for various combinations of true geometric standard deviation (1.2 to 4), percent censoring (1% to 50%), and sample size (5 to 19 and 20 to 100). Each method was used to estimate four parameters of the lognormal distribution: (1) the geometric mean, GM; (2) geometric standard deviation, GSD; (3) 95th percentile, and (4) Mean for the censored datasets. When estimating the GM and GSD, the bias and root mean square error (rMSE) for the beta-substitution method closely matched those for the MLE method, differing by only a small amount, which decreased with increasing sample size. When estimating the Mean and 95th percentile the beta-substitution method bias results closely matched or bettered those for the MLE method. In addition, the overall imprecision, as indicated by the rMSE, was similar to that of the MLE method when estimating the GM, GSD, 95th percentile, and Mean. The bias for the common substitution methods was highly variable, depending strongly on the range of GSD values. The beta-substitution method produced results comparable to the MLE method and is considerably easier to calculate, making it an attractive alternative. In terms of bias it is clearly superior to the commonly used LOD/2 and LOD/square root(2) substitution methods. The rMSE results for the two substitution methods were often comparable to rMSE results for the MLE method, but the substitution methods were often considerably biased.

Accurate exposure assessments are critical for ensuring that potentially hazardous exposures are properly identified and controlled. The availability and accuracy of exposure assessments can determine whether resources are appropriately allocated to engineering and administrative controls, medical surveillance, personal protective equipment and other programs designed to protect workers. A desktop study was performed using videos, task information and sampling data to evaluate the accuracy and potential bias of participants' exposure judgments. Desktop exposure judgments were obtained from occupational hygienists for material handling jobs with small air sampling data sets (0-8 samples) and without the aid of computers. In addition, data interpretation tests (DITs) were administered to participants where they were asked to estimate the 95th percentile of an underlying log-normal exposure distribution from small data sets. Participants were presented with an exposure data interpretation or rule of thumb training which included a simple set of rules for estimating 95th percentiles for small data sets from a log-normal population. DIT was given to each participant before and after the rule of thumb training. Results of each DIT and qualitative and quantitative exposure judgments were compared with a reference judgment obtained through a Bayesian probabilistic analysis of the sampling data to investigate overall judgment accuracy and bias. There were a total of 4386 participant-task-chemical judgments for all data collections: 552 qualitative judgments made without sampling data and 3834 quantitative judgments with sampling data. The DITs and quantitative judgments were significantly better than random chance and much improved by the rule of thumb training. In addition, the rule of thumb training reduced the amount of bias in the DITs and quantitative judgments. The mean DIT % correct scores increased from 47 to 64% after the rule of thumb training (P < 0.001). The accuracy for quantitative desktop judgments increased from 43 to 63% correct after the rule of thumb training (P < 0.001). The rule of thumb training did not significantly impact accuracy for qualitative desktop judgments. The finding that even some simple statistical rules of thumb improve judgment accuracy significantly suggests that hygienists need to routinely use statistical tools while making exposure judgments using monitoring data.

Particle size distributions (PSD) of industrial aerosols are usually described in the literature by a single geometric mean (GM) and geometric standard deviation (GSD) determined using log-probability (probit) analysis, thus implying the existence of a single-mode, lognormal distribution. If a multimodal distribution is suspected, as indicated in log-probability analysis by data points that do not lie upon a straight line, a different method of analysis is necessary in order to estimate the parameters of each of the underlying distributions. A simple method to analyze multimodal PSDs was developed. A microcomputer spreadsheet is used to fit a smooth curve to grouped (histogram) particle size data, e.g., cascade impactor data, scanning electron microscope (SEM) particle counts, and optical device particle counts. This method is based upon the assumption that multimodal PSDs can be adequately described by a linear combination of weighted, lognormal distributions. This method yields a fitted smooth ...

The American Conference of Governmental Industrial Hygienists (ACGIH) and the International Standards Organization (ISO) have both recently proposed similar sets of particulate sampling criteria. The criteria describe sample device collection efficiency, as a function of particle size, for inspirable, thoracic, and respirable particulate mass. The collection efficiency curves do not match the lung deposition curves at all particle sizes which leads to a bias between measured particulate (exposure) and deposited particulate (dose). This bias, as a function of particle size distribution, was examined for both the ACGIH and ISO proposed curves. Results indicate that the amount of bias varies with both geometric mean (mass median aerodynamic diameter) and geometric standard deviation. Thus, there will be a different exposure-dose relationship and exposure-response relationship for each particle size distribution. This can lead to additional variability in exposure-response analyses unless all members...

The purpose of this study was to compare the performance of several methods for statistically analyzing censored datasets [i.e. datasets that contain measurements that are less than the field limit-of-detection (LOD)] when estimating the 95th percentile and the mean of right-skewed occupational exposure data. The methods examined were several variations on the maximum likelihood estimation (MLE) and log-probit regression (LPR) methods, the common substitution methods, several non-parametric (NP) quantile methods for the 95th percentile and the NP Kaplan-Meier (KM) method. Each method was challenged with computer-generated censored datasets for a variety of plausible scenarios where the following factors were allowed to vary randomly within fairly wide ranges: the true geometric standard deviation, the censoring point or LOD and the sample size. This was repeated for both a single-laboratory scenario (i.e. single LOD) and a multiple-laboratory scenario (i.e. three LODs) as well as a single lognormal distribution scenario and a contaminated lognormal distribution scenario. Each method was used to estimate the 95th percentile and mean for the censored datasets (the NP quantile methods estimated only the 95th percentile). For each scenario, the method bias and overall imprecision (as indicated by the root mean square error or rMSE) were calculated for the 95th percentile and mean. No single method was unequivocally superior across all scenarios, although nearly all of the methods excelled in one or more scenarios. Overall, only the MLE- and LPR-based methods performed well across all scenarios, with the robust versions generally showing less bias than the standard versions when challenged with a contaminated lognormal distribution and multiple LODs. All of the MLE- and LPR-based methods were remarkably robust to departures from the lognormal assumption, nearly always having lower rMSE values than the NP methods for the exposure scenarios postulated. In general, the MLE methods tended to have smaller rMSE values than the LPR methods, particularly for the small sample size scenarios. The substitution methods tended to be strongly biased, but in some scenarios had the smaller rMSE values, especially for sample sizes

Users of occupational exposure limits (OELs) often fail to distinguish between the complementary processes of risk assessment and exposure (risk) management. The former refers to those activities that lead to the selection of a reasonably protective exposure limit and often includes an analysis of exposure databases and an evaluation of group-based risk. The latter focuses on individual risk, and refers to those actions required of employers to ensure that each employee is unlikely to incur harm to health. This presentation focuses on how this failure to distinguish leads to misinterpretation and misuse of OELs. A typical OEL definition consists of at least three components: a concentration, an averaging time, and a target (usually the individual worker). OELs are occasionally improperly applied, resulting in a reduction of the expected level of protection. For example, sampling strategies proposed by the American Industrial Hygiene Association (AIHA) and Comité Européen de Normalisation (CEN) permit workers to be aggregated into exposure groups. Under certain circumstances this practice can leave some workers unevaluated and unprotected. Protection is also reduced when the averaging time is extended from a single shift to multiple shifts. Frequently, OELs are misinterpreted as upper limits to exposures averaged over weeks, months, or even years, rather than a single shift. Much of this confusion can be traced to the desire of some to reconcile research (epidemiology) sampling strategies with compliance sampling strategies. But the two have fundamentally different goals and objectives. Others are simply attracted to alternative OEL interpretations that permit frequent overexposures (i.e., measurements that exceed the OEL), thus making compliance easier. Given the current limitations of industrial hygiene and occupational epidemiology, and the general unwillingness of employers to routinely collect exposure data, OELs should continue to be defined as upper limits for single shift exposures. The current OEL model, which permits the use of proximate risk management goals to realize long-range objectives, should be retained. There are, however, valid reasons for augmenting this model to include criteria for evaluating compliance with long-range objectives. The augmented OEL model would be applicable to future new and revised OELs. The author suggests that OEL setting organizations consider harmonizing definitions and statistical interpretations for both existing and new OELs, thus minimizing future misinterpretation and misuse.

This study estimated the ratio of the tracheo-bronchial dust fraction to the fraction collected by a respirable dust sampler for a variety of job classifications found in conventional, continuous, and longwall coal mining sections. The ratios could then be applied in epidemiologic studies to existing respirable dust measurements to estimate thoracic mass concentrations for evaluation of the relative importance of the respirable and thoracic dust fractions to obstructive lung disease. Data collected include particle size distributions from four U.S. underground coal mines using eight-stage personal cascade impactors. A total of 180 samples were examined by mine, occupation and occupations grouped by proximity to the mine face, and by mining technology. Several fractions--that collected by the 10-mm nylon cyclone, the American Conference of Governmental Industrial Hygienists respirable and thoracic particulate mass fractions, and the estimated alveolar and tracheo-bronchial deposition fractions--were estimated. These were not significantly different when grouped by occupation, by proximity of work to the mine face, or by the type of mining technology in use. Distributions from one mine varied from the others, perhaps because it used diesel equipment in the haulage ways, which contributed to the fine aerosol fractions. Results suggest that although the tracheo-bronchial dust fraction may contribute to the development of obstructive lung disease, occupation-specific tracheo-bronchial dust fractions are not likely to produce stronger exposure-response estimates than the historically collected respirable dust concentrations.

The particle size distributions and bulk fume densities for mild steel and stainless steel welding fumes generated using two welding processes (shielded metal arc welding [SMAW] and gas metal arc welding [GMAW]) were used in mathematical models to estimate regional pulmonary deposition (the fraction of each fume expected to deposit in each region of the pulmonary system) and regional pulmonary exposure (the fraction of each fume expected to penetrate to each pulmonary region and would be collected by a particle size-selective sampling device). Total lung deposition for GMAW fumes was estimated at 60% greater than that of SMAW fumes. Considering both the potential for deposition and the fume specific surface areas, it is likely that for equal exposure concentrations GMAW fumes deliver nearly three times the particle surface area to the lungs as SMAW fumes. This leads to the hypothesis that exposure to GMAW fumes constitutes a greater pulmonary hazard than equal exposure to SMAW fumes. The implications of this hypothesis regarding the design of future health studies of welders is discussed.

Particle size distributions were measured for fumes from mild steel (MS) and stainless steel (SS); shielded metal arc welding (SMAW) and gas metal arc welding (GMAW) consumables. Up to six samples of each type of fume were collected in a test chamber using a micro-orifice uniform deposit (cascade) impactor. Bulk samples were collected for bulk fume density and specific surface area analysis. Additional impactor samples were collected using polycarbonate substrates and analyzed for elemental content. The parameters of the underlying mass distributions were estimated using a nonlinear least squares analysis method that fits a smooth curve to the mass fraction distribution histograms of all samples for each type of fume. The mass distributions for all four consumables were unimodal and well described by a lognormal distribution; with the exception of the GMAW-MS and GMAW-SS comparison, they were statistically different. The estimated mass distribution geometric means for the SMAW-MS and SMAW-SS consumables were 0.59 and 0.46 micron aerodynamic equivalent diameter (AED), respectively, and 0.25 micron AED for both the GMAW-MS and GMAW-SS consumables. The bulk fume densities and specific surface areas were similar for the SMAW-MS and SMAW-SS consumables and for the GMAW-MS and GMAW-SS consumables, but differed between SMAW and GMAW. The distribution of metals was similar to the mass distributions. Particle size distributions and physical properties of the fumes were considerably different when categorized by welding method. Within each welding method there was little difference between MS and SS fumes.