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Within the framework of Registration, Evaluation, Authorization, and restriction of CHemicals (REACH), occupational exposure models are often used to predict the levels of exposure at a workplace. Poor inter-assessor agreement with their use poses a concern that may lead to different and dangerous risk conclusions. TREXMO (v1, 2016), a web tool providing parameter translations between six exposure models under REACH, is expected to improve the inter-assessor agreement. In this study, 18 assessors performed exposure assessment for six exposure situations within and outside the framework of this translation tool. In more than half of the evaluated cases, the results showed better agreement between assessors selecting the exposure parameters within the framework of TREXMO than when manually coding. The most affected were the parameters related to activity (such as "handling types" of Stoffenmanager) and exposure control (such as local controls). Furthermore, the agreement between the estimates calculated by different assessors was also improved when performing the translations between the models. For Stoffenmanager, for example, the relative standard deviation of 70-121%, obtained for vapors without applying the translation system, was 29-94% with the translations from ART. These findings showed a potential of TREXMO to impact the inter-assessor agreement. Because the study was limited to 18 assessors and only six exposure situations were assessed, further investigations are suggested., (© The Author(s) 2019. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

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., (Published by Oxford University Press on behalf of The British Occupational Hygiene Society 2018.)

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., (Published by Oxford University Press on behalf of The British Occupational Hygiene Society 2018.)

Several occupational exposure models are recommended under the EU's REACH legislation. Due to limited availability of high-quality exposure data, their validation is an ongoing process. It was shown, however, that different models may calculate significantly different estimates and thus lead to potentially dangerous conclusions about chemical risk. In this paper, the between-model translation rules defined in TREXMO were used to generate 319000 different in silico exposure situations in ART, Stoffenmanager, and ECETOC TRA v3. The three models' estimates were computed and the correlation and consistency between them were investigated. The best correlated pair was Stoffenmanager-ART (R, 0.52-0.90), whereas the ART-TRA and Stoffenmanager-TRA correlations were either lower (R, 0.36-0.69) or no correlation was found. Consistency varied significantly according to different exposure types (e.g. vapour versus dust) or settings (near-field versus far-field and indoors versus outdoors). The percentages of generated situations for which estimates differed by more than a factor of 100 ranged from 14 to 97%, 37 to 99%, and 1 to 68% for Stoffenmanager-ART, TRA-ART, and TRA-Stoffenmanager, respectively. Overall, the models were more consistent for vapours than for dusts and solids, near-fields than for far-fields, and indoor than for outdoor exposure. Multiple linear regression analyses evidenced the relationship between the models' parameters and the relative differences between the models' predictions. The relative difference can be used to estimate the consistency between the models. Furthermore, the study showed that the tiered approach is not generally applicable to all exposure situations. These findings emphasize the need for a multiple-model approach to assessing critical exposure scenarios under REACH. Moreover, in combination with occupational exposure measurements, they might also be used for future studies to improve prediction accuracy., (© The Author(s) 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

The Advanced REACH Tool (ART) is the most sophisticated tool used for evaluating exposure levels under the European Union's Registration, Evaluation, Authorisation and restriction of CHemicals (REACH) regulations. ART provides estimates at different percentiles of exposure and within different confidence intervals (CIs). However, its performance has only been tested on a limited number of exposure data. The present study compares ART's estimates with exposure measurements collected over many years in Switzerland. Measurements from 584 cases of exposure to vapours, mists, powders, and abrasive dusts (wood/stone and metal) were extracted from a Swiss database. The corresponding exposures at the 50th and 90th percentiles were calculated in ART. To characterize the model's performance, the 90% CI of the estimates was considered. ART's performance at the 50th percentile was only found to be insufficiently conservative with regard to exposure to wood/stone dusts, whereas the 90th percentile showed sufficient conservatism for all the types of exposure processed. However, a trend was observed with the residuals, where ART overestimated lower exposures and underestimated higher ones. The median was more precise, however, and the majority (≥60%) of real-world measurements were within a factor of 10 from ART's estimates. We provide recommendations based on the results and suggest further, more comprehensive, investigations., (© The Author 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Occupational exposure models vary significantly in their complexity, purpose, and the level of expertise required from the user. Different parameters in the same model may lead to different exposure estimates for the same exposure situation. This paper presents a tool developed to deal with this concern-TREXMO or TRanslation of EXposure MOdels. TREXMO integrates six commonly used occupational exposure models, namely, ART v.1.5, STOFFENMANAGER(®) v.5.1, ECETOC TRA v.3, MEASE v.1.02.01, EMKG-EXPO-TOOL, and EASE v.2.0. By enabling a semi-automatic translation between the parameters of these six models, TREXMO facilitates their simultaneous use. For a given exposure situation, defined by a set of parameters in one of the models, TREXMO provides the user with the most appropriate parameters to use in the other exposure models. Results showed that, once an exposure situation and parameters were set in ART, TREXMO reduced the number of possible outcomes in the other models by 1-4 orders of magnitude. The tool should manage to reduce the uncertain entry or selection of parameters in the six models, improve between-user reliability, and reduce the time required for running several models for a given exposure situation. In addition to these advantages, registrants of chemicals and authorities should benefit from more reliable exposure estimates for the risk characterization of dangerous chemicals under Regulation, Evaluation, Authorisation and restriction of CHemicals (REACH)., (© The Author 2016. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

In this study, 129 exposure situations (ESs) with six or more measured inhalation exposures to dust from solids or vapour from liquids in occupational settings were compared with modelled European Centre of Ecotoxicology and Toxicology of Chemicals (ECETOC) targeted risk assessment tool, version 3 (TRAv3) estimates. The measurement data were extracted from previously published studies examining TRAv3 performance and pooled into a curated database. The comparison exercise focussed on the vapour exposure scenarios, as there were too few dust scenarios for a meaningful analysis of any required model corrections. A group of experts in the exposure modelling field retrieved and reviewed the input parameters used in these ESs. Where considered appropriate, modifications were applied to better match the input parameter definitions and the scope of applicability of the TRAv3. Differences and mean absolute error (MAE) were calculated between the log-transformed modelled exposure value and the 75th percentile of each measured data set and regression analysis was performed. The results indicated that the TRAv3 overestimated 80% of the measured data sets. Both over- and underestimations were mostly by factors 1–5. The calculated MAE for liquids was 0.7, indicating that on average the difference between the 75th percentile and the TRAv3 estimate was less than one order of magnitude. A multiple linear regression showed that some input parameters such as medium volatility, certain process categories (PROC), industrial setting, and the presence of local exhaust ventilation are associated with underestimations. The results of the regression analysis can be used by TRAv3 users to review the degree of over- or underestimation in their current exposure assessments, compared to the curated database. Although multiple linear regression is an appropriate methodology to characterize the TRAv3's performance, more data sets are still needed in view of some remaining data gaps. Nevertheless, the results of the current analysis are being used by ECETOC to further develop the tool as a suitably conservative screening tool for use in REACH chemical safety assessment of occupational exposure to chemicals. [ABSTRACT FROM AUTHOR]