In the scope of a Dutch programme to reinforce the working conditions policy on hazardous substances, an internet-based tool was developed to help small- and medium-sized companies to handle hazardous substances with more care. The heart of this tool, called the Stoffenmanager, is a risk banding scheme. It combines a hazard banding scheme similar to that of COSHH Essentials and an exposure banding scheme based on an exposure model originally presented by Cherrie et al. (1996) and further developed by Cherrie and Schneider (1999). The exposure model has been modified to allow non-expert users to understand and use the model. Exposure scores are calculated based on categorization of determinants of emission, transmission and immission. These exposure scores are assigned to exposure bands. The comparison of exposure bands and hazard bands leads to a risk band or priority band. Following the evaluation of the priority of tasks done with products, generic exposure control measures can be evaluated for their possibility to lower the risks. Relevant control measures can be put into an action plan and into workplace instruction cards. The tool has several other functionalities regarding registration and storage of products. The exposure model in the Stoffenmanager leads to exposure scores. These have been compared with measured exposure levels. The exposure scores correlated well with measured exposure levels. The development of the Stoffenmanager has facilitated a whole range of further developments of useful tools for small- and medium-sized enterprises.

Objectives For regulatory risk assessment under REACH a tiered approach is proposed in which the first tier models should provide a conservative exposure estimate that can discriminate between scenarios which are of concern and those which are not. The Stoffenmanager is mentioned as a first tier approach in the REACH guidance. In an attempt to investigate the validity of the Stoffenmanager algorithms, a cross-validation study was performed. Methods Exposure estimates using the Stoffenmanager algorithms were compared with exposure measurement results (n=254). Correlations between observed and predicted exposures, bias and precision were calculated. Stratified analyses were performed for the scenarios “handling of powders and granules” (n=82), “handling solids resulting in comminuting” (n=60), “handling of low-volatile liquids” (n=40) and “handling of volatile liquids” (n=72). Results The relative bias of the four algorithms ranged between −9% and −77% with a precision of approximately 1.7. The 90th percentile estimate of one out of four algorithms was not conservative enough. Based on these statistics and analyses of residual plots the underlying algorithm was adapted. Subsequently, the calibration and the cross-validation dataset were merged into one dataset (n=952) used for calibrating the adapted Stoffenmanager algorithms. This new calibration resulted in new exposure algorithms for the four scenarios. Conclusions The Stoffenmanager is capable of discriminating among exposure levels mainly between scenarios in different companies. The 90th percentile estimates of the Stoffenmanager are verified to be sufficiently conservative. Therefore, the Stoffenmanager could be a useful tier 1 exposure assessment tool for REACH.

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 ≤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.

On 20 October 2020, the Working Group “Exposure Models” of the Europe Regional Chapter of the International Society of Exposure Science (ISES Europe) organised an online workshop to discuss the theoretical background of models for the assessment of occupational exposure to chemicals. In this report, participants of the workshop with an active role before and during the workshop summarise the most relevant discussion points and conclusions of this well-attended workshop. ISES Europe has identified exposure modelling as one priority area for the strategic development of exposure science in Europe in the coming years. This specific workshop aimed to discuss the main challenges in developing, validating, and using occupational-exposure models for regulatory purposes. The theoretical background, application domain, and limitations of different modelling approaches were presented and discussed, focusing on empirical “modifying-factor” or “mass-balance-based” approaches. During the discussions, these approaches were compared and analysed. Possibilities to address the discussed challenges could be a validation study involving alternative modelling approaches. The wider discussion touched upon the close relationship between modelling and monitoring and the need for better linkage of the methods and the need for common monitoring databases that include data on model parameters.

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.

Control Banding (CB) strategies to prevent work-related illness and injury for 2.5 billion workers without access to health and safety professionals has grown exponentially this last decade. CB originates from the pharmaceutical industry to control active pharmaceutical ingredients without a complete toxicological basis and therefore no occupational exposure limits. CB applications have broadened into chemicals in general - including new emerging risks like nanomaterials and recently into ergonomics and injury prevention. CB is an action-oriented qualitative risk assessment strategy offering solutions and control measures to users through "toolkits". Chemical CB toolkits are user-friendly approaches used to achieve workplace controls in the absence of firm toxicological and quantitative exposure information. The model (technical) validation of these toolkits is well described, however firm operational analyses (implementation aspects) are lacking. Consequentially, it is often not known if toolkit use leads to successful interventions at individual workplaces. This might lead to virtual safe workplaces without knowing if workers are truly protected. Upcoming international strategies from the World Health Organization Collaborating Centers request assistance in developing and evaluating action-oriented procedures for workplace risk assessment and control. It is expected that to fulfill this strategy's goals, CB approaches will continue its important growth in protecting workers.