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For many work situations only insufficient exposure data are available to perform proper risk assessment. Because measuring worker exposure can be time consuming and resource intense, the availability of reliable exposure models is important when performing risk assessments. However, the development and improvement of exposure models are hampered by scarcity of sound exposure data as well as by lack of information on relevant exposure factors and conditions of exposure. This paper describes a study where inhalation and dermal exposure data were collected under defined conditions. Exposure scenarios examined included tasks that have not been investigated in previous validation studies. The results of these measurements were compared with ECETOC TRA model version 3.1 predictions. In this study, five exposure scenarios were selected, namely 'use in a closed batch process' (PROC 4), 'mixing or blending in a partly open batch process' (PROC 5), 'rolling' (PROC 10), 'immersion' (PROC 13), and 'stirring' (PROC 19). These PROCs stem from the descriptors that Registration, Evaluation and Authorization of Chemicals has established to depict the identified uses of chemical substances. These exposure scenarios were selected mainly because little or no data are available for these situations, or ECETOC TRA is likely to underestimate exposure for these situations. Experiments were performed by volunteers for the selected exposure scenarios, in which tasks were performed aiming to represent real workplace situations. In total 70 experiments were performed, during which 70 dermal exposure measurements (5 volunteers × 2 repeats × 7 scenarios) and 32 inhalation exposure measurements (4 volunteers × 2 repeats × 4 scenarios) were collected. Two formulations were used, namely pure Tinopal SWN powder (solid product, a fluorescent tracer) and 0.5% Tinopal SWN dissolved in 1,2-dichloroethane (1,2-DCE). DCE is considered a moderate volatile liquid. For exposure scenarios using the liquid formulation, both inhalation and dermal measurements were performed, while for exposure scenarios using the pure powder only dermal exposure measurements were performed. In addition, photographs were taken under ultraviolet light to qualitatively assess exposure patterns on hands and body. Volunteers repeatedly performed a selection of tasks under standardized conditions in a test chamber for each exposure scenario. Results show that ECETOC TRA overestimated dermal hand exposure for all PROCs included in the study, and was considered to be conservative. Additionally, ECETOC TRA overestimated inhalation exposure for closed and partially closed processes, but underestimated inhalation exposure for rolling and handling of immersed objects. Qualitative assessment of the hands and body showed mainly the hands were exposed for tasks involving closed and partially closed processes and when handling of immersed objects. Exposure to other body segments were also observed for rolling and stirring. In conclusion, this study gave insights into dermal and inhalation exposure levels during selected task scenarios, and showed that ECETOC TRA is conservative when dermal exposure is estimated. Inhalation exposure estimates for PROCs 10 and 13 tasks with the moderate volatility liquid were underestimated in this study. It may be therefore necessary to re-evaluate base model predictions for these scenarios when medium fugacity liquids are involved., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Measured data are generally preferred to modelled estimates of exposure. Grouping and read-across is already widely used and accepted approach in toxicology, but an appropriate approach and guidance on how to use existing exposure measurement data on one substance and work situation for another substance and/or work situation is currently not available. This study presents a framework for an extensive read-across of existing worker inhalable exposure measurement data. This framework enables the calculation of read-across factors based on another substance and/or work situation by first evaluating the quality of the existing measurement data and then mapping its similarity or difference with another substance and/or work situation. The system of read-across factors was largely based on the determinants in ECETOC TRA and ART exposure models. The applicability of the framework and its proof of principle were demonstrated by using five case studies. In these case studies, either the 75th percentiles of measured exposure data was observed to lie within the estimated 90% confidence intervals from the read-across approach or at least with the increase in the geometric mean of measured exposure, geometric mean of estimated exposure also increased. Testing and re-evaluation of the present framework by experts in exposure assessment and statistics is recommended to develop it further into a tool that can be widely used in exposure assessment and regulatory practices., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Model tools for estimating hazardous substance exposure are an accepted part of regulatory risk assessments in Europe, and models underpin control banding tools used to help manage chemicals in workplaces. Of necessity the models are simplified abstractions of real-life working situations that aim to capture the essence of the scenario to give estimates of actual exposures with an appropriate margin of safety. The basis for existing inhalation exposure assessment tools has recently been discussed by some scientists who have argued for the use of more complex models. In our opinion, the currently accepted tools are documented to be the most robust way for workplace health and safety practitioners and others to estimate inhalation exposure. However, we recognise that it is important to continue the scientific development of exposure modelling to further elaborate and improve the existing methodologies.

In The Netherlands, the web-based tool called 'Stoffenmanager' was initially developed to assist small- and medium-sized enterprises to prioritize and control risks of handling chemical products in their workplaces. The aim of the present study was to explore the accuracy of the Stoffenmanager exposure algorithm. This was done by comparing its semi-quantitative exposure rankings for specific substances with exposure measurements collected from several occupational settings to derive a quantitative exposure algorithm. Exposure data were collected using two strategies. First, we conducted seven surveys specifically for validation of the Stoffenmanager. Second, existing occupational exposure data sets were collected from various sources. This resulted in 378 and 320 measurements for solid and liquid scenarios, respectively. The Spearman correlation coefficients between Stoffenmanager scores and exposure measurements appeared to be good for handling solids (r(s) = 0.80, N = 378, P < 0.0001) and liquid scenarios (r(s) = 0.83, N = 320, P < 0.0001). However, the correlation for liquid scenarios appeared to be lower when calculated separately for sets of volatile substances with a vapour pressure >10 Pa (r(s) = 0.56, N = 104, P < 0.0001) and non-volatile substances with a vapour pressure < or =10 Pa (r(s) = 0.53, N = 216, P < 0.0001). The mixed-effect regression models with natural log-transformed Stoffenmanager scores as independent parameter explained a substantial part of the total exposure variability (52% for solid scenarios and 76% for liquid scenarios). Notwithstanding the good correlation, the data show substantial variability in exposure measurements given a certain Stoffenmanager score. The overall performance increases our confidence in the use of the Stoffenmanager as a generic tool for risk assessment. The mixed-effect regression models presented in this paper may be used for assessment of so-called reasonable worst case exposures. This evaluation is considered as an ongoing process and when more good quality data become available, the analyses described in this paper will be expanded. Based on these analyses, the algorithm will be refined in the near future.

Background: The authors recently developed a Dermal Exposure Assessment Method (DREAM), an observational semiquantitative method to assess dermal exposures by systematically evaluating exposure determinants using pre-assigned default values., Aim: To explore the accuracy of the DREAM method by comparing its estimates with quantitative dermal exposure measurements in several occupational settings., Methods: Occupational hygienists observed workers performing a certain task, whose exposure to chemical agents on skin or clothing was measured quantitatively simultaneously, and filled in the DREAM questionnaire. DREAM estimates were compared with measurement data by estimating Spearman correlation coefficients for each task and for individual observations. In addition, mixed linear regression models were used to study the effect of DREAM estimates on the variability in measured exposures between tasks, between workers, and from day to day., Results: For skin exposures, spearman correlation coefficients for individual observations ranged from 0.19 to 0.82. DREAM estimates for exposure levels on hands and forearms showed a fixed effect between and within surveys, explaining mainly between-task variance. In general, exposure levels on clothing layer were only predicted in a meaningful way by detailed DREAM estimates, which comprised detailed information on the concentration of the agent in the formulation to which exposure occurred., Conclusions: The authors expect that the DREAM method can be successfully applied for semiquantitative dermal exposure assessment in epidemiological and occupational hygiene surveys of groups of workers with considerable contrast in dermal exposure levels (variability between groups >1.0). For surveys with less contrasting exposure levels, quantitative dermal exposure measurements are preferable.

Workplace dermal exposure assessment is a complex task that aims to understand the dynamic interaction between the skin and the hazardous substances present in the surrounding environment. A European project known as RISKOFDERM gathered dermal exposure data in 85 workplaces (industrial and other types) in five countries in Europe. In order to optimize data collection and to develop a representative picture of dermal exposure, scenarios (tasks made up of a series of activities) were grouped together into dermal exposure operation units (DEOs). The allocation of scenarios to relevant DEOs was achieved on the basis of similarities of exposure routes, tasks and professional judgement. Sampling and quantification procedures were based on the approaches recommended by the OECD protocol. The laboratories involved in the analysis of the samples participated in quality assurance programmes. This exercise resulted in 419 body measurements and 437 measurements on hands expressed in terms of formulation (product) in use. Exposures for a given scenario varied by several orders of magnitude. The extent and patterns of exposure were found to be dependent on various exposure determinants, including inter- and intra-scenario variations. Hands were found to be the most contaminated parts of the body. Exposure patterns for liquid and solid contaminants were different. On the basis of the analysis of the data presented here, the averaged results (median and 95th percentile) for a given DEO unit should not be used as a representative measure of dermal exposure for all scenarios within that DEO without taking the exposure determinants into account. However, the data could be used to develop an exposure matrix (indicative exposure distributions) for different types of scenario and workplace, using determinants of exposure and a Bayesian approach to integrating expert opinion.