This commentary explores the use of high-resolution data from new, miniature sensors to enrich models that predict exposures to chemical substances in the workplace. To optimally apply these sensors, one can expect an increased need for new models that will facilitate the interpretation and extrapolation of the acquired time-resolved data. We identified three key modelling approaches in the context of sensor data, namely (i) enrichment of existing time-integrated exposure models, (ii) (new) high-resolution (in time and space) empirical models, and (iii) new ‘occupational dispersion’ models. Each approach was evaluated in terms of their application in research, practice, and for policy purposes. It is expected that substance-specific sensor data will have the potential to transform workplace modelling by re-calibrating, refining, and validating existing (time-integrated) models. An increased shift towards ‘sensor-driven’ models is expected. It will allow for high-resolution modelling in time and space to identify peak exposures and will be beneficial for more individualized exposure assessment and real-time risk management. New ‘occupational dispersion models’ such as interpolation, computational fluid dynamic models, and assimilation techniques, together with sensor data, will be specifically useful. These techniques can be applied to develop site-specific concentration maps which calculate personal exposures and mitigate worker exposure through early warning systems, source finding and improved control design and control strategies. Critical development and investment needs for sensor data linked to (new) model development were identified such as (i) the generation of more sensor data with reliable sensor technologies (achieved by improved specificity, sensitivity, and accuracy of sensors), (ii) investing in statistical and new model developments, (iii) ensuring that we comply with privacy and security issues of concern, and (iv) acceptance by relevant target groups (such as employers and employees) and stimulation of these new technologies by policymakers and technology developers.

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.

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®.

Exposure to asbestos fibres is linked to numerous adverse health effects and the use of asbestos is currently banned in many countries. Still, asbestos applications are present in numerous residential and professional/industrial buildings or installations which need to be removed. Exposure measurements give good insight in exposure levels on the basis of which the required control regime is determined to ensure that workers are protected against adverse health effects. However, it is a costly and time-consuming process to measure all situations as working conditions and materials may vary greatly. Therefore, the mechanistic model ‘Asbestos Removal Exposure Assessment Tool (AREAT)’ was developed to estimate exposure to respirable asbestos fibres released during asbestos abatement processes where measurements are not available. In such instances tailored control regimes can be implemented based on modelled exposure levels. The mechanistic model was developed using scientific literature, an in-house asbestos abatement dataset, and knowledge with regard to previously developed models. Several exposure determinants such as the substance emission potential, activity emission potential, control measures, and dilution in air were identified and specific modifiers were developed for each category. Through an algorithm, AREAT calculates a dimensionless score based on the model inputs. The model was calibrated using a statistical model on an extensive measurement dataset containing a broad variety of exposure scenarios. This statistical model enabled the translation of dimensionless AREAT scores to actual estimated fibre concentrations in fibres m−3. In total, 370 personal inhalation exposure measurements from 71 different studies were used for calibration of AREAT. Of these measurements, in 191 cases (52%) with microscopic analysis (all asbestos fibre analyses were conducted with scanning electron microscopy/energy dispersive X-ray analysis in accordance with ISO 14966) no fibres were detected and the limit of detection values(LODs) were given. To assess the influence of the large number of measurements with exposures below LOD values on the performance of the model, calibrations were performed on the total dataset and the selection of data excluding measurements below LOD. The AREAT model correlated well with the datasets, with a Pearson correlation of 0.73 and 0.8 and Spearman rank correlation of 0.56 and 0.8. The model was fitted to estimate a typical exposure value [i.e. geometric mean (GM) exposures], but it is recommended to use a more conservative worst case higher percentile (for example the 90th percentile; which adds a factor of 17.3 based on the model uncertainty on the GM estimate), to account for variability in the measurements and uncertainty in model estimates. This work has shown the development and calibration of a mechanistic model, capable of estimating asbestos fibre exposures during asbestos abatement processes. The AREAT model will be implemented as a lower tier exposure model in a risk assessment tool used within the Netherlands to plan abatement processes and to develop control strategies.