Abstract 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.