Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices.

Advanced material development, including at the nanoscale, comprises costly and complex challenges coupled to ensuring human and environmental safety. Governmental agencies regulating safety have announced interest toward acceptance of safety data generated under the collective term New Approach Methodologies (NAMs), as such technologies/approaches offer marked potential to progress the integration of safety testing measures during innovation from idea to product launch of nanomaterials. Divided in overall eight main categories, searchable databases for grouping and read across purposes, exposure assessment and modeling, in silico modeling of physicochemical structure and hazard data, in vitro high-throughput and high-content screening assays, dose-response assessments and modeling, analyses of biological processes and toxicity pathways, kinetics and dose extrapolation, consideration of relevant exposure levels and biomarker endpoints typify such useful NAMs. Their application generally agrees with articulated stakeholder needs for improvement of safety testing procedures. They further fit for inclusion and add value in nanomaterials risk assessment tools. Overall 37 of 50 evaluated NAMs and tiered workflows applying NAMs are recommended for considering safer-by-design innovation, including guidance to the selection of specific NAMs in the eight categories. An innovation funnel enriched with safety methods is ultimately proposed under the central aim of promoting rigorous nanomaterials innovation.

Comparison of methods for converting Dylos particle number concentrations to PM2.5 mass concentrations.

The aim of this study was to (a) develop a method for converting particle number concentrations (PNC) obtained by Dylos to PM2.5 mass concentrations, (b) compare this conversion with similar methods available in the literature, and (c) compare Dylos PM2.5 obtained using all available conversion methods with gravimetric samples. Data were collected in multiple residences in three European countries using the Dylos and an Aerodynamic Particle Sizer (APS, TSI) in the Netherlands or an optical particle counter (OPC, GRIMM) in Greece. Two statistical fitted curves were developed based on Dylos PNC and either an APS or an OPC particle mass concentrations (PMC). In addition, at the homes of 16 volunteers (UK and Netherlands), Dylos measurements were collected along with gravimetric samples. The Dylos PNC were transformed to PMC using all the fitted curves obtained during this study (and three found in the literature) and were compared with gravimetric samples. The method developed in the present study using an OPC showed the highest correlation (Pearson (R) = 0.63, Concordance (ρc ) = 0.61) with gravimetric data. The other methods resulted in an underestimation of PMC compared to gravimetric measurements (R = 0.65-0.55, ρc  = 0.51-0.24). In conclusion, estimation of PM2.5 concentrations using the Dylos is acceptable for indicative purposes.

Validation and Recalibration of the Asbestos Removal Exposure Assessment Tool (AREAT).

OBJECTIVES: The Asbestos Removal Exposure Assessment Tool (AREAT) was previously developed to estimate exposure to respirable asbestos fibres during abatement processes. The current study describes the validation and recalibration of the AREAT model with external data. During model validation, the AREAT model was expanded to be able to estimate asbestos exposure from an additional source category: 'unspecified asbestos remnants'. METHODS: The validation dataset (n = 281) was derived from exposure measurement studies where for each exposure measurement the AREAT model parameters were coded and estimates were calculated. Pearson correlation coefficients (r) and intra class correlation coefficients (icc) were calculated as an indication of the agreement between the AREAT estimates and measured concentrations. In addition, the bias and the proportion of measurements with higher concentrations than model estimates were calculated. To expand and investigate model performance on exposure from 'unspecified asbestos remnants', a separate dataset was created with measurements collected during working with unspecified asbestos remnants, and similar validation comparisons were performed. Lastly, linear regression techniques were used to investigate possible improvements in model parameters. The model was recalibrated on a combined dataset consisting of the validation dataset and the original calibration dataset to increase model robustness. RESULTS: The validation comparisons showed good relative agreement (r) between AREAT estimates and measurements (r = 0.73) and a moderate absolute agreement (icc = 0.53). The overall relative bias was 108%, indicating an overall overestimation of exposure, and 4% of the estimated concentrations were higher than the actual measured concentrations. For the data subset concerning unspecified asbestos remnants, a moderate correlation between model estimates and measurement outcomes was found (r = 0.63). However, based on the low number of data in this subset, and moderate r, it was decided that cleaning of unspecified asbestos remnants is out of scope until more data are available. The results of this validation study suggested that two input parameters (product type friable material, efficacy of control measure foam) underestimated exposure. The effects of these parameters were updated to improve model performance. Compared to the original model, the recalibrated model resulted in slightly higher explained variance (62% compared to 56%) and lower uncertainty (15 compared to 17.3). CONCLUSION: The original AREAT model provided reliable asbestos exposure estimates with a sufficient level of conservatism taking into account the 90-percentile estimates. The model was further improved via the addition of a new feature and recalibration to predict asbestos exposure during the clean-up of unspecified asbestos remnants.

TRAAC framework to improve regulatory acceptance and wider usability of tools and methods for safe innovation and sustainability of manufactured nanomaterials.

There has been an increasing use of advanced materials, particularly manufactured nanomaterials, in industrial applications and consumer products in the last two decades. It has instigated concerns about the sustainability, in particular, risks and uncertainties regarding the interactions of the manufactured nanomaterials with humans and the environment. Consequently, significant resources in Europe and beyond have been invested into the development of tools and methods to support risk mitigation and risk management, and thus facilitate the research and innovation process of manufactured nanomaterials. The level of risk analysis is increasing, including assessment of socio-economic impacts, and sustainability aspects, moving from a conventional risk-based approach to a wider safety-and-sustainability-by-design perspective. Despite these efforts on tools and methods development, the level of awareness and use of most of such tools and methods by stakeholders is still limited. Issues of regulatory compliance and acceptance, reliability and trust, user-friendliness and compatibility with the users' needs are some of the factors which have been traditionally known to hinder their widespread use. Therefore, a framework is presented to quantify the readiness of different tools and methods towards their wider regulatory acceptance and downstream use by different stakeholders. The framework diagnoses barriers which hinder regulatory acceptance and wider usability of a tool/method based on their Transparency, Reliability, Accessibility, Applicability and Completeness (TRAAC framework). Each TRAAC pillar consists of criteria which help in evaluating the overall quality of the tools and methods for their (i) compatibility with regulatory frameworks and (ii) usefulness and usability for end-users, through a calculated TRAAC score based on the assessment. Fourteen tools and methods were assessed using the TRAAC framework as proof-of-concept and for user variability testing. The results provide insights into any gaps, opportunities, and challenges in the context of each of the 5 pillars of the TRAAC framework. The framework could be, in principle, adapted and extended to the evaluation of other type of tools & methods, even beyond the case of nanomaterials.

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®.

The Development and Calibration of a Mechanistic Asbestos Removal Exposure Assessment Tool (AREAT).

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.

Future Prospects of Occupational Exposure Modelling of Substances in the Context of Time-Resolved Sensor Data.

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.

Prediction of Dermal Exposure to Chemical Substances Using a Fluorescence Method within the SysDEA Project.

Dermal exposure is an important exposure route for occupational exposure and risk assessment. A fluorescence method has been developed to quantify occupational dermal exposure based on a visualization technique, using Tinopal SWN as a fluorescent tracer. The method was developed within the framework of a large experimental study, the SysDEA project. In SysDEA, dermal exposure was measured with different methods for 10 simulated exposure situations by sampling powder and liquid formulations containing Tinopal SWN on coveralls and patches and subsequently chemically analysing them. For the fluorescence method, photographs of exposed volunteers who performed the experiments were taken inside a room which consisted of an optimized arrangement of several UV irradiating tube light brackets, reflective and non-reflective backgrounds for maximum light diffusion and a camera. Image processing analysis software processed these photographs to obtain corresponding light intensity in terms of summed pixel values. To be able to estimate the amount of Tinopal SWN, 25% of the measured data from the SysDEA experiments were used to calibrate by correlating the summed pixel values from the photographs to actual measured exposure values using a second order regression model. For spraying both high and low viscosity liquids, showing uniformly distributed exposure patterns, strong Pearson correlation coefficients (R > 0.77) were observed. In contrast, the correlations were either inconsistently poor (R = -0.17 to 0.28 for pouring, rolling high viscosity liquid, manually handling objects immersed in low viscosity liquid and handling objects contaminated with powder), moderate (R = 0.73 for dumping of powder), or strong (R = 0.83 and 0.77 for rolling low viscosity liquid and manually handling objects immersed in high viscosity liquid). A model for spraying was developed and calibrated using 25% of the available experimental data for spraying and validated using the remaining 75%. Under given experimental conditions, the fluorescence method shows promising results and can be used for the quantification of dermal exposure for different body parts (excluding hands) for spraying-like scenarios that have a more uniform exposure pattern, but more research is needed for exposure scenarios with less uniform exposure patterns. For the estimation of exposure levels, the surface loading limit should be lower than 1.5░µg/cm2 (a lower limit could not be quantified based on experiments conducted in this study) on a large surface, like a coverall, which should be ideally perpendicular to the camera.

Extrapolating the Applicability of Measurement Data on Worker Inhalation Exposure to Chemical Substances.

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.

Experimental Assessment of Inhalation and Dermal Exposure to Chemicals During Industrial or Professional Activities in Relation to the Performance of ECETOC TRA.

For many work situations only insufficient exposure data are available to perform proper risk assessment. Because measuring worker exposure can be time consuming and resource intense, the availability of reliable exposure models is important when performing risk assessments. However, the development and improvement of exposure models are hampered by scarcity of sound exposure data as well as by lack of information on relevant exposure factors and conditions of exposure. This paper describes a study where inhalation and dermal exposure data were collected under defined conditions. Exposure scenarios examined included tasks that have not been investigated in previous validation studies. The results of these measurements were compared with ECETOC TRA model version 3.1 predictions. In this study, five exposure scenarios were selected, namely 'use in a closed batch process' (PROC 4), 'mixing or blending in a partly open batch process' (PROC 5), 'rolling' (PROC 10), 'immersion' (PROC 13), and 'stirring' (PROC 19). These PROCs stem from the descriptors that Registration, Evaluation and Authorization of Chemicals has established to depict the identified uses of chemical substances. These exposure scenarios were selected mainly because little or no data are available for these situations, or ECETOC TRA is likely to underestimate exposure for these situations. Experiments were performed by volunteers for the selected exposure scenarios, in which tasks were performed aiming to represent real workplace situations. In total 70 experiments were performed, during which 70 dermal exposure measurements (5 volunteers × 2 repeats × 7 scenarios) and 32 inhalation exposure measurements (4 volunteers × 2 repeats × 4 scenarios) were collected. Two formulations were used, namely pure Tinopal SWN powder (solid product, a fluorescent tracer) and 0.5% Tinopal SWN dissolved in 1,2-dichloroethane (1,2-DCE). DCE is considered a moderate volatile liquid. For exposure scenarios using the liquid formulation, both inhalation and dermal measurements were performed, while for exposure scenarios using the pure powder only dermal exposure measurements were performed. In addition, photographs were taken under ultraviolet light to qualitatively assess exposure patterns on hands and body. Volunteers repeatedly performed a selection of tasks under standardized conditions in a test chamber for each exposure scenario. Results show that ECETOC TRA overestimated dermal hand exposure for all PROCs included in the study, and was considered to be conservative. Additionally, ECETOC TRA overestimated inhalation exposure for closed and partially closed processes, but underestimated inhalation exposure for rolling and handling of immersed objects. Qualitative assessment of the hands and body showed mainly the hands were exposed for tasks involving closed and partially closed processes and when handling of immersed objects. Exposure to other body segments were also observed for rolling and stirring. In conclusion, this study gave insights into dermal and inhalation exposure levels during selected task scenarios, and showed that ECETOC TRA is conservative when dermal exposure is estimated. Inhalation exposure estimates for PROCs 10 and 13 tasks with the moderate volatility liquid were underestimated in this study. It may be therefore necessary to re-evaluate base model predictions for these scenarios when medium fugacity liquids are involved.

Comparison of Measurement Methods for Dermal Exposure to Hazardous Chemicals at the Workplace: The SysDEA Project.

There is a principal need for more precise methodology with regard to the determination of occupational dermal exposure. The goal of the Systematic analysis of Dermal Exposure to hazardous chemical Agents at the workplace project was therefore to generate scientific knowledge to improve and standardize measurement methods for dermal exposure to chemicals at the workplace. In addition, the comparability of different measurement methods was investigated. Different methods (body sampling by means of coveralls and patches, hand sampling by means of gloves and washing, and head sampling by means of headbands and wiping) were compared. Volunteers repeatedly performed a selection of tasks under standardized conditions in test chambers to increase the reproducibility and decrease variability. The selected tasks were pouring, rolling, spraying, and handling of objects immersed in liquid formulations, as well as dumping and handling objects contaminated with powder. For the chemical analysis, the surrogate test substance Tinopal SWN was analyzed by means of a high-performance liquid chromatographic method using a fluorescence detector. Tinopal SWN was either applied as a solid product in its pure form, or as a low and high viscosity liquid containing Tinopal SWN in dissolved form. To compare the sampling methods with patches and coveralls, the exposure values as measured on the patches were extrapolated to the surface areas of the respective parts of the coverall. Based on this extrapolation approach, using the patch method resulted in somewhat higher exposure values compared to using a coverall for all exposure situations, but the differences were only statistically significant in case of the liquid exposure situations. Using gloves resulted in significantly higher exposure values compared to hand wash for handling immersed objects, rolling, and handling contaminated objects, and slightly higher (not significant) exposure values during pouring and spraying. In the same context, applying wipe sampling resulted in higher exposure values than using a headband, which was at least partly due to extrapolation of the wipe results to the surface area of the headband. No 'golden standard' with regard to a preferred measurement method for dermal exposure could be identified from the methods as investigated in the current study.

Validation of the dermal exposure model in ECETOC TRA.

The ECETOC TRA model (presently version 3.1) is often used to estimate worker inhalation and dermal exposure in regulatory risk assessment. The dermal model in ECETOC TRA has not yet been validated by comparison with independent measured exposure levels. This was the goal of the present study. Measured exposure levels and relevant contextual information were gathered via literature search, websites of relevant occupational health institutes and direct requests for data to industry. Exposure data were clustered in so-called exposure cases, which are sets of data from one data source that are expected to have the same values for input parameters in the ECETOC TRA dermal exposure model. For each exposure case, the 75th percentile of measured values was calculated, because the model intends to estimate these values. The input values for the parameters in ECETOC TRA were assigned by an expert elicitation and consensus building process, based on descriptions of relevant contextual information.From more than 35 data sources, 106 useful exposure cases were derived, that were used for direct comparison with the model estimates. The exposure cases covered a large part of the ECETOC TRA dermal exposure model. The model explained 37% of the variance in the 75th percentiles of measured values. In around 80% of the exposure cases, the model estimate was higher than the 75th percentile of measured values. In the remaining exposure cases, the model estimate may not be sufficiently conservative.The model was shown to have a clear bias towards (severe) overestimation of dermal exposure at low measured exposure values, while all cases of apparent underestimation by the ECETOC TRA dermal exposure model occurred at high measured exposure values. This can be partly explained by a built-in bias in the effect of concentration of substance in product used, duration of exposure and the use of protective gloves in the model. The effect of protective gloves was calculated to be on average a factor of 34 in this data set, while factors of five to ten were used in the model estimations. There was also an effect of the sampling method in the measured data on the exposure levels. Exposure cases where sampling was done via an interception method, such as gloves, on average showed a factor of six higher 75th percentiles of measured values than exposure cases where sampling was done via a removal method, such as hand washing. This may partly be responsible for the apparent underestimation of dermal exposure by the model at high exposure values. However, there also appeared to be a relation between expected exposure level (as indicated by the model estimate) and the choice of sampling method.In this study, solid substances used in liquid products were treated as liquids with negligible volatility. The results indicate that the ECETOC TRA dermal exposure model performs equally well for these substances as for liquids. There were suggestions of a difference in performance of the model between solids and liquids.For several parts of the ECETOC TRA dermal model, no or hardly any measured dermal exposure data were available. Therefore, gathering of more dermal exposure levels is recommended, specifically for situations not yet sufficiently covered in the present data set.