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.

Estimating cobalt exposure in respirable dust from cobalt in inhalable dust.

Cobalt is a commonly used element in metal industry. Exposure to workers occurs mainly by inhalation of cobalt-containing dust. For the evaluation of cobalt exposure, risk assessment and investigations on occupational diseases, measurements of cobalt in respirable dust are needed. Up to now, often only data for cobalt in inhalable dust are available, which is due to the earlier classification of the limit value in this fraction. Therefore, a possibility to convert cobalt concentrations mathematically from inhalable into respirable concentrations is desirable. In this study, 639 parallel measurements of cobalt concentrations in inhalable (cI(Co)) and respirable dust fractions (cR(Co)) were extracted from the non-public exposure database MEGA (Measurement data relating to workplace exposure to hazardous substances, maintained at the Institute for Occupational Safety and Health of the German Social Accident Insurance) and investigated by regression analysis. For the whole dataset regression shows high quality measures (correlation coefficient R = 0.888, adjusted coefficient of determination adj. R2 = 0.788 - R2 is adjusted to sample size). Further description of the data is achieved by splitting the dataset according to the type of sampling ('stationary' and 'personal') and three working activity groups, 'high temperature processing', 'filling/transport/storage', and 'machining/abrasive techniques' (0.845 ≤ R ≤ 0.876; 0.711 ≤ adj. R2 ≤ 0.762). As subgroups of 'high temperature processing' and 'machining/abrasive techniques' two further groups could be determined. These groups are called heuristic groups, since they have to be formed non-systematically by trial and error. These heuristic groups are 'welding' and 'grinding'. They are more selective on the included working activities with adj. R2 of 0.703 and 0.748 respectively. The resulting conversion functions of all groups are power functions with exponents between 0.704 and 0.794. For the estimation of cobalt in respirable dust in other studies, it is possible to use the conversion functions of the heuristic and working activity groups. Limitations of the possibility to use the conversion functions are discussed.

Response Letter to Koivisto et al. 'Evaluating the Theoretical Background of STOFFENMANAGER® and the Advanced REACH Tool'.

In this article, we have responded to the key statements in the article by Koivisto et al. (2022) that were incorrect and considered to be a biased critique on a subset of the exposure models used in Europe (i.e. ART and Stoffenmanager®) used for regulatory exposure assessment. We welcome scientific discussions on exposure modelling (as was done during the ISES Europe workshop) and criticism based on scientific evidence to contribute to the advancement of occupational exposure estimation tools. The tiered approach to risk assessment allows various exposure assessment models from screening tools (control/hazard banding) through to higher-tiered approaches. There is a place for every type of model, but we do need to recognize the cost and data requirements of highly bespoke assessments. That is why model developers have taken pragmatic approaches to develop tools for exposure assessments based on imperfect data. We encourage Koivisto et al. to focus on further scientifically robust work to develop mass-balance models and by independent external validations studies, compare these models with alternative model tools such as ART and Stoffenmanager®.

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.

Estimating nickel exposure in respirable dust from nickel in inhalable dust.

The conversion of dust components is of high importance for retrospective evaluations of exposure levels, of occupational diseases or the time trend of occupational dust exposure. For this purpose, possibilities to convert nickel concentrations from inhalable to respirable dust are discussed in this study. Therefore, 551 parallel measurements of nickel concentrations in inhalable and respirable dust fractions were extracted from the exposure database MEGA (maintained at the Institute for Occupational Safety and Health of the German Social Accident Insurance) and investigated by linear regression analysis of ln-transformed concentrations. Inhalable dust is the most important predictor variable, showing an adjusted coefficient of determination (adj. R2) of 0.767 (R2 adjusted to sample size). Since multilinear regression analysis, cannot be applied, further description of data is gained by splitting the whole dataset into working activity groups (e. g. 'high temperature processing', adj. R2 = 0.628,' filling/transport/storage' adj. R2 = 0.741, 'machining/abrasive techniques', adj. R2 = 0.777). From these groups, four task restrictive subgroups, so-called 'heuristic groups', can be derived by pooling similar working tasks with similar regression coefficients and enhanced quality measures (adj. R2 between 0.724 and 0.924): 'welding (grinding time fraction [GTF] < 5%)', 'welding (grinding time fraction [GTF] > 5%)', 'high temperature cutting' and 'grinding'. For the working activity group 'high temperature processing' and the heuristic group 'welding' the determination of the grinding time fraction and its inclusion or exclusion from a dataset has a huge impact on the description of data and whether a transformation of nickel concentrations using the natural logarithm (ln) is necessary or not. In case of GTF < 5%, the conversions functions are linear, all other conversion functions are power functions with exponents between 0.713 and 0.986. It is possible to develop conversion functions for estimating the nickel concentration in the respirable dust fraction (cR(Ni)) out of the nickel concentration in the inhalable dust fraction (cI(Ni)). For the estimation of Nickel in respirable dust other studies, it is recommend to use the conversion functions of the heuristic trial and error groups. Limitations of the possibility to use the conversion functions are discussed.

Estimating Respirable Dust Exposure from Inhalable Dust Exposure.

In the sector of occupational safety and health only a limited amount of studies are concerned with the conversion of inhalable to respirable dust. This conversion is of high importance for retrospective evaluations of exposure levels or of occupational diseases. For this reason a possibility to convert inhalable into respirable dust is discussed in this study. To determine conversion functions from inhalable to respirable dust fractions, 15 120 parallel measurements in the exposure database MEGA (maintained at the Institute for Occupational Safety and Health of the German Social Accident Insurance) are investigated by regression analysis. For this purpose, the whole data set is split into the influencing factors working activity and material. Inhalable dust is the most important predictor variable and shows an adjusted coefficient of determination of 0.585 (R2 adjusted to sample size). Further improvement of the model is gained, when the data set is split into six working activities and three material groups (e.g. high temperature processing, adj. R2 = 0.668). The combination of these two variables leads to a group of data concerned with high temperature processing with metal, which gives rise to a better description than the whole data set (adj. R2 = 0.706). Although it is not possible to refine these groups further systematically, seven improved groups are formed by trial and error, with adj. R2 between 0.733 and 0.835: soldering, casting (metalworking), welding, high temperature cutting, blasting, chiseling/embossing, and wire drawing. The conversion functions for the seven groups are appropriate candidates for data reconstruction and retrospective exposure assessment. However, this is restricted to a careful analysis of the working conditions. All conversion functions are power functions with exponents between 0.454 and 0.946. Thus, the present data do not support the assumption that respirable and inhalable dust are linearly correlated in general.

Exposure Models for REACH and Occupational Safety and Health Regulations.

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.

Modelling of exposure to respirable and inhalable welding fumes at German workplaces.

The International Agency for Research on Cancer classified welding fumes as carcinogenic to humans, and occupational exposure limits should be established to protect welders. The aim of this study is to estimate exposure levels to inhalable and respirable welding fumes by welding process to use them for exposure assessment in epidemiological studies and to derive occupational exposure limits. In total, 15,473 mass concentrations of inhalable and 9,161 concentrations of respirable welding fumes could be analyzed along with welding-related and sampling information, which were compiled in the German database MEGA between 1983 and 2016. In both particle-size fractions, model-based geometric means of the concentrations were estimated by welding process and material for frequently used welding processes adjusted for sampling time and median-centered for calendar years. The inhalable concentrations were approximately twice the respirable concentrations, with medians of 3 mg/m3 (inter-quartile range: 1.2-7.0 mg/m3) and 1.5 mg/m3 (inter-quartile range: < limit of detection -3.8 mg/m3), respectively. The adjusted geometric means of flux-cored arc welding, metal inert and active gas welding, shielded metal arc welding and torch cutting ranged from 0.9 to 2.2 mg/m3 for respirable welding fumes and from 2.3 to 4.7 mg/m3 for inhalable fumes. In both particle-size fractions, geometric means were between 0.1 and 0.9 mg/m3 when performing tungsten inert gas, autogeneous, resistance, laser, and plasma welding or spraying. Results derived from this large dataset are useful for a quantitative exposure assessment to estimate health risks of welders.

Modelling of occupational exposure to inhalable nickel compounds.

The aim of this study was to estimate average occupational exposure to inhalable nickel (Ni) using the German exposure database MEGA. This database contains 8052 personal measurements of Ni collected between 1990 and 2009 in adjunct with information on the measurement and workplace conditions. The median of all Ni concentrations was 9 µg/m3 and the 95th percentile was 460 µg/m3. We predicted geometric means (GMs) for welders and other occupations centered to 1999. Exposure to Ni in welders is strongly influenced by the welding process applied and the Ni content of the used welding materials. Welding with consumable electrodes of high Ni content (>30%) was associated with 10-fold higher concentrations compared with those with a low content (<5%). The highest exposure levels (GMs ≥20 µg/m3) were observed in gas metal and shielded metal arc welders using welding materials with high Ni content, in metal sprayers, grinders and forging-press operators, and in the manufacture of batteries and accumulators. The exposure profiles are useful for exposure assessment in epidemiologic studies as well as in industrial hygiene. Therefore, we recommend to collect additional exposure-specific information in addition to the job title in community-based studies when estimating the health risks of Ni exposure.

Hazard banding in compliance with the new Globally Harmonised System (GHS) for use in control banding tools.

Many control banding tools use hazard banding in risk assessments for the occupational handling of hazardous substances. The outcome of these assessments can be combined with advice for the required risk management measures (RMMs). The Globally Harmonised System of Classification and Labelling of Chemicals (GHS) has resulted in a change in the hazard communication elements, i.e. Hazard (H) statements instead of Risk-phrases. Hazard banding schemes that depend on the old form of safety information have to be adapted to the new rules. The purpose of this publication is to outline the rationales for the assignment of hazard bands to H statements under the GHS. Based on this, this publication proposes a hazard banding scheme that uses the information from the safety data sheets as the basis for assignment. The assignment of hazard bands tiered according to the severity of the underlying hazards supports the important principle of substitution. Additionally, the set of assignment rules permits an exposure-route-specific assignment of hazard bands, which is necessary for the proposed route-specific RMMs. Ideally, all control banding tools should apply the same assignment rules. This GHS-compliant hazard banding scheme can hopefully help to establish a unified hazard banding strategy in the various control banding tools.

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.

Uranium mining in Germany: incidence of occupational diseases 1946-1999.

OBJECTIVES: In East Germany, uranium mining was performed on a large scale for approximately 45 years (1946-1990). In particular, the poor working conditions during the post-war years until 1955 led to a high level of occupational diseases. The present study gives an overview of the occurrence of occupational diseases during the mining period as well as after uranium mining was stopped in 1990. METHODS: The number of occupational diseases which occurred during the mining period was calculated from the files of the former Wismut SDAG. Although exposure to uranium ceased after 1990, new cases of occupational diseases were recognized after that date. These were recorded by the German Federation Of Institutions For Statutory Accident Insurance And Prevention (HVBG). RESULTS AND CONCLUSIONS: Today, more than 35,000 cases of occupational diseases are known, and many more are expected. About two-thirds of them are lung diseases: 16,376 cases of silicosis/silicotuberculosis and 7,695 cases of bronchial carcinomas. The increase in the number of recognized occupational diseases is shown and discussed against the background of changes in criteria for recognition and in working conditions as well as the duration of the latency period.