Evaluation of recommended REACH exposure modeling tools and near-field, far-field model in assessing occupational exposure to toluene from spray paint

Predictive modeling is an available tool to assess worker exposures to a variety of chemicals in different industries and product-use scenarios. The European Chemical Agency (ECHA)'s guidelines for manufacturers to fulfill the European Union's legal requirements pursuant to the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) initiative include recommendations for the use of modeling to predict worker exposures. ECHA recommends different models for different target populations (i.e. workers, consumers, environment) and routes of exposure (i.e. skin absorption, ingestion, inhalation), and presents them hierarchically, with Tier 1 models presented as the most simplistic, conservative models and Tier 2 models recommended for further intensive evaluation of substances or preparations. In order to assess these models for one exposure (product-use) scenario, a simulation of the scenario was completed in a controlled environment and the measured results were compared with the modeling outputs. The authors predicted, based on the design of the modeling tools, that all models would overestimate worker exposures under the simulated product-use scenario, with the lower-tiered model producing the most conservative estimate of exposure. In this study, a Tier 1 model and a Tier 2 model were evaluated for comparison with the near-field, far-field (NF-FF) deterministic model and measured experimental results in a real-time worker inhalation exposure assessment. Modeling was conducted prior to actual air monitoring. The exposure scenario that was evaluated involved the application of a toluene-containing spray paint to a work surface. Air samples were collected to evaluate short-term (15-min) and long-term (240-min) exposures. Eight-hour time-weighted averages (8-h TWAs) were calculated and compared with the modeling outputs from the recommended REACH modeling tools and the NF-FF model. A comparison of each of the modeling tools with measured experimental results was generated. The Tier 1 Targeted Risk Assessment tool overestimated the 8-h TWA airborne concentration of toluene in the spray scenario by a factor of 3.61. The higher tiered Advanced REACH Tool and NF-FF models showed greater concordance with experimental results, overestimating the TWA exposure by a factor of 2.92 and 1.96, respectively. In conclusion, the Tier 1 and 2 exposure modeling tools performed as expected for the simulated exposure scenario, providing relatively accurate, though conservative, estimates according to the level of detail and precision accounted for in each model.

Use of the MEGA exposure database for the validation of the Stoffenmanager model

OBJECTIVES

This paper explores the usefulness of the exposure database MEGA for model validation and evaluates the capability of two Stoffenmanager model equations (i.e. handling of powders/granules and machining) to estimate workers exposure to inhalable dust.

METHODS

For the task groups, 'handling of powders and granules' (handling) and 'machining of wood and stone' (machining) measurements were selected from MEGA and grouped in scenarios depending on task, product, and control measures. The predictive capability of the model was tested by calculating the relative bias of the single measurements and the correlation between geometric means (GMs) for scenarios. The conservatism of the model was evaluated by checking if the percentage of measurement values above the 90th percentile estimate was ≤10%.

RESULTS

From 22 596 personal measurements on inhalable dust within MEGA, 390 could be selected for handling and 1133 for machining. The relative bias for the task groups was -25 and 68%, respectively, the percentage of measurements with a higher result than the estimated 90th percentile 11 and 7%. Correlations on a scenario level were good for both model equations as well for the GM (handling: r(s) = 0.90, n = 15 scenarios; machining: r(s) = 0.84, n = 22 scenarios) as for the 90th percentile (handling: r(s) = 0.79; machining: r(s) = 0.76).

CONCLUSIONS

The MEGA database could be used for model validation, although the presented analyses have learned that improvements in the database are necessary for modelling purposes in the future. For a substantial amount of data, contextual information on exposure determinants in addition to basic core information is stored in this database. The relative low bias, the good correlation, and the level of conservatism of the tested model show that the Stoffenmanager can be regarded as a useful Tier 1 model for the Registration, Evaluation, Authorisation and Restriction of Chemicals legislation.

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.