Exposure modelling in Europe: how to pave the road for the future as part of the European Exposure Science Strategy 2020-2030.

Exposure models are essential in almost all relevant contexts for exposure science. To address the numerous challenges and gaps that exist, exposure modelling is one of the priority areas of the European Exposure Science Strategy developed by the European Chapter of the International Society of Exposure Science (ISES Europe). A strategy was developed for the priority area of exposure modelling in Europe with four strategic objectives. These objectives are (1) improvement of models and tools, (2) development of new methodologies and support for understudied fields, (3) improvement of model use and (4) regulatory needs for modelling. In a bottom-up approach, exposure modellers from different European countries and institutions who are active in the fields of occupational, population and environmental exposure science pooled their expertise under the umbrella of the ISES Europe Working Group on exposure models. This working group assessed the state-of-the-art of exposure modelling in Europe by developing an inventory of exposure models used in Europe and reviewing the existing literature on pitfalls for exposure modelling, in order to identify crucial modelling-related strategy elements. Decisive actions were defined for ISES Europe stakeholders, including collecting available models and accompanying information in a living document curated and published by ISES Europe, as well as a long-term goal of developing a best-practices handbook. Alongside these actions, recommendations were developed and addressed to stakeholders outside of ISES Europe. Four strategic objectives were identified with an associated action plan and roadmap for the implementation of the European Exposure Science Strategy for exposure modelling. This strategic plan will foster a common understanding of modelling-related methodology, terminology and future research in Europe, and have a broader impact on strategic considerations globally.

Theoretical Background of Occupational-Exposure Models-Report of an Expert Workshop of the ISES Europe Working Group "Exposure Models".

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.

An Intervention Study on the Implementation of Control Banding in Controlling Exposure to Hazardous Chemicals in Small and Medium-sized Enterprises.

BACKGROUND: Management and workers in small and medium-sized enterprises (SMEs) often find it hard to comprehend the requirements related to controlling risks due to exposure to substances. An intervention study was set up in order to support 45 SMEs in improving the management of the risks of occupational exposure to chemicals, and in using the control banding tool and exposure model Stoffenmanager in this process. METHODS: A 2-year intervention study was carried out, in which a mix of individual and collective training and support was offered, and baseline and effect measurements were carried out by means of structured interviews, in order to measure progress made. A seven-phase implementation evolutionary ladder was used for this purpose. Success and failure factors were identified by means of company visits and structured interviews. RESULTS: Most companies clearly moved upwards on the implementation evolutionary ladder; 76% of the companies by at least one phase, and 62% by at least two phases. Success and failure factors were described. CONCLUSION: Active training and coaching helped the participating companies to improve their chemical risk management, and to avoid making mistakes when using and applying Stoffenmanager. The use of validated tools embedded in a community platform appears to support companies to organize and structure their chemical risk management in a business-wise manner, but much depends upon motivated occupational health and safety (OHS) professionals, management support, and willingness to invest time and means.

Stoffenmanager Nano version 1.0: a web-based tool for risk prioritization of airborne manufactured nano objects.

Stoffenmanager Nano (version 1.0) is a risk-banding tool developed for employers and employees to prioritize health risks occurring as a result of exposure to manufactured nano objects (MNOs) for a broad range of worker scenarios and to assist implementation of control measures to reduce exposure levels. In order to prioritize the health risks, the Stoffenmanager Nano combines the available hazard information of a substance with a qualitative estimate of potential for inhalation exposure. The development of the Stoffenmanager Nano started with a review of the available literature on control banding. Input parameters for the hazard assessment of MNOs were selected based on the availability of these parameters in, for instance, Safety Data Sheets or product information sheets. The conceptual exposure model described by Schneider et al. (2011) was used as the starting point for exposure banding. During the development of the Stoffenmanager Nano tool, the precautionary principle was applied to deal with the uncertainty regarding hazard and exposure assessment of MNOs. Subsequently, the model was converted into an online tool (http://nano.stoffenmanager.nl), tested, and reviewed by a number of companies. In this paper, we describe the Stoffenmanager Nano. This tool offers a practical approach for risk prioritization in exposure situations where quantitative risk assessment is currently not possible. Updates of this first version are anticipated as more data become available in the future.

Cross-validation and refinement of the Stoffenmanager as a first tier exposure assessment tool for REACH.

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.

'Stoffenmanager', a web-based control banding tool using an exposure process model.

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.

Stoffenmanager exposure model: development of a quantitative algorithm.

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.