Adoption of Exposure Assessment Tools to Assist in Providing Respiratory Protection Recommendations.

Selecting a proper respirator requires determining the ratio of an employee's maximum use concentration (MUC) divided by the occupational exposure limit of a chemical. Current industrial hygiene practice often is to obtain a percentile estimate (e.g. 95th) of the measured exposure distribution to apply as the MUC. However, practitioners who are not yet familiar with statistical or mathematical approaches may choose the highest exposure data point as the MUC, a method that is still considered appropriate by the Occupational Safety and Health Administration. Nonetheless, choosing a respirator using the highest exposure data point when only limited data are available may result in not always providing the most adequate respirator. Because some practitioners are not familiar with exposure assessment tools, our primary goal in this study was to demonstrate the best process when selecting respiratory protection by using a combination of exposure data and assessment tools. Three user-friendly tools, IHDataAnalyst, Advanced REACH Tool, and IHSTAT, were selected to demonstrate how to use different types of tool outputs when choosing a respirator. A decision logic was developed to help users navigate the combining of different data inputs. Personal full-shift exposure data collected in four different workplaces were used to describe four different outcomes generated when the maximum exposure data point and the tool's output are compared with the exposure limit of the chemical. Outcomes varied, from determinations of 'high confidence' (or final decision) to 'low confidence' (or indicating more data are needed) in the selection of a respirator recommendation. In conclusion, systematically adopting the combination of exposure data and assessment tools could increase practitioners' confidence in decision-making when choosing respirators from a limited exposure data set. These suggested guidelines will lead practitioners toward good industrial hygiene practices.

TREXMO plus: an advanced self-learning model for occupational exposure assessment.

In Europe, several occupational exposure models have been developed and are recommended for regulatory exposure assessment. Only some information on the substance of interest (e.g., vapor pressure) and the workplace conditions (e.g., ventilation rate) is required in these models to predict an exposure value that will be later used to characterize the risk. However, it has been shown that models may differ in their predictions and that usually, one of the models best fits a given set of exposure conditions. Unfortunately, there are no clear rules on how to select the best model. In this study, we developed a new modeling approach that together uses the three most popular models, Advanced REACH Tool, Stoffenmanger, and ECETOC TRAv3, to obtain a unique exposure prediction. This approach is an extension of the TREXMO tool, and is called TREXMO+. TREXMO+ applies a machine-learning technique on a set of exposure data with the measured values to split them into smaller subsets, corresponding to exposure conditions sharing similar characteristics. For each subset, TREXMO+ then establishes a regression model with the three REACH tools used as the exposure predictors. The performance of the new model was tested and a comparison was made between the results obtained by TREXMO+ and those obtained by conventional tools. TREXMO+ model was found to be less biased and more accurate than the REACH models. Its prediction differs generally from measurements by a factor of 2-3 from measurements, whereas conventional models were found to differ by a factor 2-14. However, as the available test dataset is limited, its results will need to be confirmed by larger-scale tests.

Evaluation of Exposure Assessment Tools under REACH: Part I-Tier 1 Tools.

Tier 1 occupational exposure assessment tools recommended for use under the Registration, Evaluation, Authorization, and restriction of CHemicals (REACH) were evaluated using newly collected measurement data. Evaluated tools included the ECETOC TRAv2 and TRAv3, MEASEv1.02.01, and EMKG-EXPO-TOOL. Fifty-three exposure situations (ESs) based on tasks/chemicals were developed from National Institute for Occupational Safety and Health field surveys. During the field surveys, high quality contextual information required for evaluating the tools was also collected. For each ES, applicable tools were then used to generate exposure estimates using a consensus approach. Among 53 ESs, only those related to an exposure category of liquids with vapor pressure (VP) > 10 Pa had sufficient numbers of exposure measurements (42 ESs with n = 251 for TRAv2 and TRAv3 and 40 ESs with n = 243 for EMKG-EXPO-TOOL) to be considered in detail. The results for other exposure categories (aqueous solutions, liquids with VP ≤ 10 Pa, metal processing, powders, and solid objects) had insufficient measurement to allow detailed analyses (results listed in the Supplementary File). Overall, EMKG-EXPO-TOOL generated more conservative results than TRAv2 and TRAv3 for liquids with high VP. This finding is at least partly due to the fact that the EMKG-EXPO-TOOL only considers pure substances and not mixtures of chemical agents. For 34 out of 40 ESs available for chemicals with VP > 10 Pa, the liquid was a mixture rather than a pure substance. TRAv3 was less conservative than TRAv2, probably due to additional refinement of some input parameters. The percentages of exposure measurement results exceeding the corresponding tool estimates for liquids with VP > 10 Pa by process category and by input parameters were always higher for TRAv3 compared to those for TRAv2. Although the conclusions of this study are limited to liquids with VP > 10 Pa and few process categories, this study utilized the most transparent contextual information compared to previous studies, reducing uncertainty from assumptions for unknown input parameters. A further validation is recommended by collecting sufficient exposure data covering other exposure categories and all process categories under REACH.

Evaluation of Exposure Assessment Tools under REACH: Part II-Higher Tier Tools.

Stoffenmanager®v4.5 and Advanced REACH Tool (ART) v1.5, two higher tier exposure assessment tools for use under REACH, were evaluated by determining accuracy and robustness. A total of 282 exposure measurements from 51 exposure situations (ESs) were collected and categorized by exposure category. In this study, only the results of liquids with vapor pressure (VP) > 10 Pa category having a sufficient number of exposure measurements (n = 251 with 42 ESs) were utilized. In addition, the results were presented by handling/activity description and input parameters for the same exposure category. It should be noted that the performance results of Stoffenmanager and ART in this study cannot be directly compared for some ESs because ART allows a combination of up to four subtasks (and nonexposed periods) to be included, whereas the database for Stoffenmanager, separately developed under the permission of the legal owner of Stoffenmanager, permits the use of only one task to predict exposure estimates. Thus, it would be most appropriate to compare full-shift measurements against ART predictions (full shift including nonexposed periods) and task-based measurements against task-based Stoffenmanager predictions. For liquids with VP > 10 Pa category, Stoffenmanager®v4.5 appeared to be reasonably accurate and robust when predicting exposures [percentage of measurements exceeding the tool's 90th percentile estimate (%M > T) was 15%]. Areas that could potentially be improved include ESs involving the task of handling of liquids on large surfaces or large work pieces, allocation of high and medium VP inputs, and absence of local exhaust ventilation input. Although the ART's median predictions appeared to be reasonably accurate for liquids with VP > 10 Pa, the %M > T for the 90th percentile estimates was 41%, indicating that variance in exposure levels is underestimated by ART. The %M > T using the estimates of the upper value of 90% confidence interval (CI) of the 90th percentile estimate (UCI90) was considerably reduced to 18% for liquids with VP > 10 Pa. On the basis of this observation, users might be to consider using the upper limit value of 90% CI of the 90th percentile estimate for predicting reasonable worst case situations. Nevertheless, for some activities and input parameters, ART still shows areas to be improved. Hence, it is suggested that ART developers review the assumptions in relation to exposure variability within the tool, toward improving the tool performance in estimating percentile exposure levels. In addition, for both tools, only some handling/activity descriptions and input parameters were considered. Thus, further validation studies are still necessary.

Exposure models for the prior distribution in bayesian decision analysis for occupational hygiene decision making.

This study introduces two semi-quantitative methods, Structured Subjective Assessment (SSA) and Control of Substances Hazardous to Health (COSHH) Essentials, in conjunction with two-dimensional Monte Carlo simulations for determining prior probabilities. Prior distribution using expert judgment was included for comparison. Practical applications of the proposed methods were demonstrated using personal exposure measurements of isoamyl acetate in an electronics manufacturing facility and of isopropanol in a printing shop. Applicability of these methods in real workplaces was discussed based on the advantages and disadvantages of each method. Although these methods could not be completely independent of expert judgments, this study demonstrated a methodological improvement in the estimation of the prior distribution for the Bayesian decision analysis tool. The proposed methods provide a logical basis for the decision process by considering determinants of worker exposure.

Evaluation of the COSHH Essentials model with a mixture of organic chemicals at a medium-sized paint producer.

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.

Exposure assessment by physiologic sampling pump--prediction of minute ventilation using a portable respiratory inductive plethysmograph system.

A study was conducted to evaluate a portable respiratory inductive plethysmograph (RIP) as a means to estimate minute ventilation (V(E)) for use in controlling the flow rate of a physiologic sampling pump (PSP). Specific aims were to: (1) evaluate the ability of the portable RIP system to measure V(E) using a direct (individual) fixed-volume calibration method (Direct RIP model), (2) develop and evaluate the performance of indirect (group) regression models for V(E) prediction using output data from the portable RIP and subject demographic characteristics (Indirect RIP model), and (3) compare V(E) estimates from indirect and direct portable RIP calibration with indirect estimation models published previously. Nine subjects (19-44 years) were divided into calibration (n = 6) and test (n = 3) datasets and performed step-tests on three different days while wearing the portable RIP and breathing through a pneumotachometer (reference). Minute ventilation and portable RIP output including heart rate, breathing rate, and a motion index were recorded simultaneously during the 80 minute sessions. Calibration data were used to develop a regression model for V(E) prediction that was subsequently applied to the test dataset. Direct calibration of the portable RIP system produced highly variable estimates of V(E) (R2 = 0.62, average % error = 15 +/- 50) while Indirect RIP model results were highly correlated with the reference (R2 = 0.80-0.88) and estimates of total volume were within 10% of reference values on average. Although developed from a limited dataset, the Indirect RIP model provided an alternative approach to estimation of V(E) and total volume with accuracy comparable to previously published models.