Advanced REACH Tool Research Articles

Comparison between measured and modelled data on occupational exposure to organic solvents in degreasing, varnishing and painting activities performed in a Romanian company

This study aims to highlight the similarities and differences between occupational exposure measured (personal and static) and modelled data using the Tier 2 Advanced REACH Tool (ART 1.5) for some organic solvents used in degreasing, varnishing and painting activities. The measurements were performed between 2020 and 2022 in a Romanian company manufacturing metallic components for the energy industry. The conclusion of the study will allow identifying some of the uncertainties related to the use of modelled data versus measured data, and to decide on the adequate operational conditions (OCs) and risk management measures (RMMs) to implement in place, in accordance with the hierarchy of control principles.

How Valuable Are Small Measurement Datasets in Supplementing Occupational Exposure Models? A Numerical Study Using the Advanced Reach Tool

The Advanced REACH Tool (ART) is the most detailed exposure model currently available for estimating inhalation exposures to dusts, vapours, and aerosols under a broad range of exposure scenarios. The ART follows a Bayesian approach, making use of a calibrated source-receptor model to provide central estimates of exposures and information on exposure variability from meta-analyses in the literature. Uniquely amongst exposure models, the ART provides a facility to update the baseline estimates from the mechanistic model and variance components using measurement data collected on the exposure scenario; however, in practical use, this facility is little used. In this paper, the full capability of the ART tool is demonstrated using a small number of carefully chosen case studies that each had a sufficient breadth of personal exposure measurement data to support a measurement-led exposure assessment. In total, six cases studies are documented, three where the estimate from the source-receptor model of the ART was consistent with measurement data, and a further three case studies where the source-receptor model of the ART was inconsistent with measurement data, resulting in a prior-data conflict. A simulation study was designed that involved drawing subsets of between two and ten measurements from the available measurement dataset, with estimates of the geometric mean (GM) and 90th percentile of exposures from the posterior distribution of ART compared against measurement-based estimates of these summaries. Results from this work indicate that very substantial reductions in the uncertainty associated with estimates of the GM and 90th percentile could be achieved with as few as two measurements, with results in detail sensitive to both the measurements themselves and worker and company labels associated with the measurements. For case studies involving prior-data conflicts, the estimates of the GM and 90th percentile rapidly changed as measurement data were used to update the prior. However, results suggest that the current statistical model of the ART does not allow a complete resolution of a prior-data conflict.

Evaluation of Stoffenmanager® and ART for Estimating Occupational Inhalation Exposures to Volatile Liquids.

In practice, workers often handle the same chemical(s) of interest under different control measures (e.g. local ventilation, enclosed system) during a full shift. Stoffenmanager® allows users to predict either task-based or full-shift exposures. However, most previous studies evaluated the tool by comparing task-based exposures with measured exposures. Also, limited evaluation studies of the Advanced REACH Tool (ART) with the Bayesian approach (ART+B) are available, requiring additional evaluation studies. The performance of Stoffenmanager® and ART with and without the Bayesian approach was evaluated with measured full-shift exposures to volatile liquids in terms of accuracy, precision, and conservatism. Forty-two exposure situation scenarios (including 251 exposures), developed based on job tasks and chemicals handled during tasks from workplaces, were used to generate full-shift estimates. The estimates were then compared with measured exposures using various comparison methods. Overall, Stoffenmanager® appeared to be the most accurate among the testing tools, while ART+B was the most precise. The percentage of measured exposures exceeding the tools' 90th percentile estimates (%M>T) demonstrated that Stoffenmanager® (16%M>T) and ART+B (13%M>T) were more conservative than ART (41%M>T). When the 90% upper confidence limit of the 90th percentile estimate was considered, the level of conservatism changed from low (41%M>T) to medium (17%M>T) for ART and from medium (13%M>T) to high (0.8%M>T) for ART+B. The findings of this study indicate that no single tool would work for all ESs. Thus, it is recommended that users select a tool based on the performance results of three components (i.e. accuracy, precision, and conservatism), not depending on one or two components. The strength of this study is that the required tools' input parameters were obtained during the sample collection to minimize assumptions for many input parameters. In addition, unlike other previous studies, multiple subtasks, which happen often in workplaces, were incorporated in this study. Nevertheless, the present study did not cover all activities listed in the tools and was limited to volatile liquids, suggesting further studies cover other exposure categories (e.g. solid, metal) and diverse activities.

The Dermal Advanced REACH Tool (dART): A Bayesian Model for Dermal Exposure Assessment.

The dermal Advanced REACH Tool (dART) is a tier 2 exposure model for estimating dermal exposure to the hands (mg min-1) for non-volatile liquid and solid-in-liquid products. The dART builds upon the existing ART framework and describes three mass transport processes (deposition (Dhands), direct emission and direct contact (Ehands), and contact transfer (Thands)) that may each contribute to dermal exposure. The mechanistic model that underpins the dART and calibration of the mechanistic model, such that the dimensionless score that results from encoding contextual information about a task into the determinants of the dART can be converted into a prediction of exposure (mg min-1), have been described in previous work. This paper completes the methodological framework of the dART model through placing the mechanistic model within a wider statistical modelling framework. A mixed-effects model, within a Bayesian framework, is presented for modelling the rate of dermal exposure per minute of activity. The central estimate of exposure for a particular task is provided by a calibrated mechanistic model (and thus based upon contextual information about a task). The model also describes between- and within-worker sources of variability in dermal exposure, with prior distributions for variance components based upon the literature. Estimates of exposure based upon informative prior distributions may be updated using measurement data associated with the task. The dART model is demonstrated using three worked examples, where estimates are initially obtained based upon the prior distributions alone, and then refined through accommodating measurement data on the tasks.

Evaluating the Theoretical Background of STOFFENMANAGER® and the Advanced REACH Tool.

STOFFENMANAGER® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER® and ART are based on a modified multiplicative model where an exposure base level (mg m−3) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m−3) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For example, for dusts the calibration data were pooled from exposure measurements sampled from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER® and ART. A literature review showed that the models have been validated only at the ‘operational analysis’ level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson’s correlations (r is <0.6) for both STOFFENMANAGER® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.

Comparison between Communicated and Calculated Exposure Estimates Obtained through Three Modeling Tools.

This study aims to evaluate the risk assessment approach of the REACH legislation in industrial chemical departments with a focus on the use of three models to calculate exposures, and discuss those factors that can determine a bias between the estimated exposure (and therefore the expected risk) in the extended safety data sheets (e-SDS) and the expected exposure for the actual scenario. To purse this goal, the exposure estimates and risk characterization ratios (RCRs) of registered exposure scenarios (ES; “communicated exposure” and “communicated RCR”) were compared with the exposure estimates and the corresponding RCRs calculated for the actual, observed ES, using recommended tools for the evaluation of exposure assessment and in particular the following tools: (i) the European Centre for Ecotoxicology and Toxicology of Chemicals Targeted Risk Assessment v.3.1 (ECETOC TRA), (ii) STOFFENMANAGER® v.8.0 and (iii) the Advanced REACH Tool (ART). We evaluated 49 scenarios in three companies handling chemicals. Risk characterization ratios (RCRs) were calculated by dividing estimated exposures by derived no-effect levels (DNELs). Although the calculated exposure and RCRs generally were lower than communicated, the correlation between communicated and calculated exposures and RCRs was generally poor, indicating that the generic registered scenarios do not reflect actual working, exposure and risk conditions. Further, some observed scenarios resulted in calculated exposure values and RCR higher than those communicated through chemicals’ e-SDSs; thus ‘false safe’ scenarios (calculated RCRs > 1) were also observed. Overall, the obtained evidences contribute to doubt about whether the risk assessment should be performed using generic (communicated by suppliers) ES with insufficient detail of the specific scenario at all companies. Contrariwise, evidences suggested that it would be safer for downstream users to perform scenario-specific evaluations, by means of proper scaling approach, to achieve more representative estimates of chemical risk.

Calibration of the Welding Advanced REACH Tool (weldART).

This paper reports a study to develop and calibrate a deterministic model of welding fume exposure based on a four-compartment mass-balance model - The Welding Advanced REACH Tool (weldART). To achieve this aim, measurements of welding fume exposure were collected along with data on exposure determinants needed in the modelling. The welding fume exposure data was obtained from workers in a structural steel fabrication plant. Welders were engaged in three processes: flux-cored arc welding (FCAW), shielded metal arc welding (SMAW) and gas tungsten arc welding (GTAW). Aerosol concentration was measured using 13mm diameter Swinnex sampling heads and MicroPEM direct-reading aerosol monitors. The model was initially developed with three spatial compartments (near-field (NF), far-field (FF), and welding plume (WP)). However, in the welding scenario investigated the FF had a very large volume and it was necessary to subdivide the room volume into an intermediate zone representing the FF along with the remaining room zone (RM). We fitted linear equations forced through the origin to the gravimetric concentrations measured inside the welders' visor and the weldART model estimates. The flowrates between the model compartments were adjusted by trial and error to obtain proportionate concentrations in each compartment. The FCAW process generated higher welding fume particulate concentrations than SMAW and GTAW. The MicroPEM monitors considerably underestimated and were poorly correlated with the corresponding data from the Swinnex samplers. It was concluded that the MicroPEM data were unreliable. The model calibration showed a strong association between the personal exposure measurement and the weldART model values (R2=0.94), with the average estimated value 1.3 times the measurements. The NF and the FF model estimates were poorly correlated with the corresponding compartment measurements (R2=0.37 and 0.35, respectively), although on average the model estimates were close to the measurement data (ratio of modelled to measured 0.9, and 1.0, respectively). The calibration shows that the weldART model is able to predict the exposure of welding fume particulate.

Adoption of Exposure Assessment Tools to Assist in Providing Respiratory Protection Recommendations.

Selecting a proper respirator requires determining the ratio of an employee's maximum use concentration (MUC) divided by the occupational exposure limit of a chemical. Current industrial hygiene practice often is to obtain a percentile estimate (e.g. 95th) of the measured exposure distribution to apply as the MUC. However, practitioners who are not yet familiar with statistical or mathematical approaches may choose the highest exposure data point as the MUC, a method that is still considered appropriate by the Occupational Safety and Health Administration. Nonetheless, choosing a respirator using the highest exposure data point when only limited data are available may result in not always providing the most adequate respirator. Because some practitioners are not familiar with exposure assessment tools, our primary goal in this study was to demonstrate the best process when selecting respiratory protection by using a combination of exposure data and assessment tools. Three user-friendly tools, IHDataAnalyst, Advanced REACH Tool, and IHSTAT, were selected to demonstrate how to use different types of tool outputs when choosing a respirator. A decision logic was developed to help users navigate the combining of different data inputs. Personal full-shift exposure data collected in four different workplaces were used to describe four different outcomes generated when the maximum exposure data point and the tool's output are compared with the exposure limit of the chemical. Outcomes varied, from determinations of 'high confidence' (or final decision) to 'low confidence' (or indicating more data are needed) in the selection of a respirator recommendation. In conclusion, systematically adopting the combination of exposure data and assessment tools could increase practitioners' confidence in decision-making when choosing respirators from a limited exposure data set. These suggested guidelines will lead practitioners toward good industrial hygiene practices.

TREXMO plus: an advanced self-learning model for occupational exposure assessment.

In Europe, several occupational exposure models have been developed and are recommended for regulatory exposure assessment. Only some information on the substance of interest (e.g., vapor pressure) and the workplace conditions (e.g., ventilation rate) is required in these models to predict an exposure value that will be later used to characterize the risk. However, it has been shown that models may differ in their predictions and that usually, one of the models best fits a given set of exposure conditions. Unfortunately, there are no clear rules on how to select the best model. In this study, we developed a new modeling approach that together uses the three most popular models, Advanced REACH Tool, Stoffenmanger, and ECETOC TRAv3, to obtain a unique exposure prediction. This approach is an extension of the TREXMO tool, and is called TREXMO+. TREXMO+ applies a machine-learning technique on a set of exposure data with the measured values to split them into smaller subsets, corresponding to exposure conditions sharing similar characteristics. For each subset, TREXMO+ then establishes a regression model with the three REACH tools used as the exposure predictors. The performance of the new model was tested and a comparison was made between the results obtained by TREXMO+ and those obtained by conventional tools. TREXMO+ model was found to be less biased and more accurate than the REACH models. Its prediction differs generally from measurements by a factor of 2-3 from measurements, whereas conventional models were found to differ by a factor 2-14. However, as the available test dataset is limited, its results will need to be confirmed by larger-scale tests.

Work Tasks as Determinants of Respirable and Inhalable Indium Exposure among Workers at an Indium-Tin Oxide Production and Reclamation Facility.

Increased global demand for touch screens, photovoltaics, and optoelectronics has resulted in an increase in the production of indium-tin oxide (ITO). Occupational exposure to indium compounds is associated with the development of indium lung disease. Although many previous epidemiologic investigations highlight an excess of lung abnormalities in workplaces where ITO is produced, few assessments of occupational exposure to respirable and inhalable indium are reported to date. The objective of this study was to identify the determinants of respirable and inhalable indium at an ITO production facility to target exposure interventions. In 2012 and 2014, we conducted exposure assessments at an ITO production facility and collected full-shift personal respirable (n = 159) and inhalable (n = 57) indium samples. We also observed workers and recorded information on task duration and location, materials used, and use of personal protective equipment (PPE). Tasks (n = 121) recorded in task diaries were categorized into 40 similar task groups using the Advanced REACH Tool and process-related information. Mixed-effects models were fit separately for log-transformed respirable and inhalable indium, with random effect of subject and fixed effects of task groups. Overall, respirable and inhalable indium measurements ranged from 0.1 to 796.6 µg m-3 and 1.6 to 10 585.7 µg m-3, respectively, and were highly correlated with Spearman correlation coefficient of 0.90. The final model for respirable indium explained 36.3% of total variance and identified sanding, powder transfer tasks in reclaim, powder transfer tasks in refinery, handling indium materials, and liquid transfer tasks in ITO production as tasks associated with increased respirable indium exposure. The final model for inhalable indium explained 24.6% of total variance and included powder transfer tasks in ITO production, cleaning cylinder or tile, and handling indium material tasks. Tasks identified as strong predictors of full-shift exposure to respirable and inhalable indium can guide the use of engineering, administrative, and PPE controls designed to mitigate occupational exposure to indium. Moreover, since the tasks were aligned with REACH activities, results from this study can also be used to inform REACH activity scenarios.

Calibration of the Dermal Advanced REACH Tool (dART) Mechanistic Model.

The dermal Advanced REACH Tool (dART) is a Tier 2 exposure modelling tool currently in development for estimating dermal exposure to the hands (mg min-1) for non-volatile liquid and solids-in-liquid products. The dART builds upon the existing ART framework and describes three mass transport processes [deposition (Dhands), direct emission and direct contact (Ehands), and contact transfer (Thands)] that may each contribute to dermal exposure. The mechanistic model that underpins the dART and its applicability domain has already been described in previous work. This paper describes the process of calibrating the mechanistic model such that the dimensionless score that results from encoding contextual information about a task into the determinants of the dART can be converted into a prediction of exposure (mg min-1). Furthermore, as a consequence of calibration, the uncertainty in a dART prediction may be quantified via a confidence interval. Thirty-six experimental studies were identified that satisfied the conditions of: (i) high-quality contextual information that was sufficient to confidently code the dART mechanistic model determinants; (ii) reliable exposure measurement data sets were available. From these studies, 40 exposure scenarios were subsequently developed. A non-linear log-normal mixed-effect model was fitted to the data set of Dhands,Ehands, and Thands scores and corresponding measurement data. The dART model was shown to be consistent with activities covering a broad range of tasks [spray applications, activities involving open liquid surfaces (e.g. dipping, mixing), handling of contaminated objects, spreading of liquid products, and transfer of products (e.g. pouring of liquid)]. Exposures resulting from a particular task were each dominated by one or two of the identified mass transport processes. As a consequence of calibration, an estimate of the uncertainty associated with a mechanistic model estimate is available. A 90% multiplicative interval is approximately a factor of six. This represents poorer overall precision than the (inhalation) ART model for dusts and vapours, although better than the ART model for mists. Considering the complexity of the conceptual model compared with the ART, the wide variety of exposure scenarios considered with differing dominant routes, and the particular challenges that result from the consideration of measurement data both above and beneath a protective glove, the precision of the calibrated dART mechanistic model is reasonable for well-documented exposure scenarios coded by experts. However, as the inputs to the model are based upon user judgement, in practical use, the reliability of predictions will be dependent upon both the competence of users and the quality of contextual information available on an exposure scenario.

Dermal Advanced REACH Tool (dART)-Development of a Dermal Exposure Model for Low-Volatile Liquids.

This article describes the development of a mechanistic model for underpinning the dermal Advanced REACH Tool (dART), an extension of the existing ART model and its software platform. It was developed for hand exposure to low volatile liquids (vapour pressure ≤ 10 Pa at 20°C) including solids-in-liquid products. The model is based on an existing conceptual dermal source-receptor model that has been integrated into the ART framework. A structured taxonomy of workplace activities referred to as activity classes are adopted from ART. Three key processes involved in mass transport associated with dermal exposure are applied, i.e. deposition, direct emission and contact, and transfer. For deposition, the model adopts all the relevant modifying factors (MFs) applied in ART. In terms of direct emission and contact (e.g. splashes) and transfer (e.g. hand-surface contacts), the model defines independent principal MFs, i.e. substance-related factors, activity-related factors, localized- and dispersion control and exposed surface area of the hands. To address event-based exposures as much as possible, the model includes crucial events during an activity (e.g. hand immersions) and translates objective information on tools and equipment (manual or automated) to probable events (e.g. splashes) and worker behaviours (e.g. surface contacts). Based on an extensive review of peer-reviewed literature and unpublished field studies, multipliers were assigned to each determinant and provide an approximated (dimensionless) numerical value. In the absence of (sufficient) evidence, multipliers were assigned to determinants based on assumptions made during discussions by experts in the consortium. A worked example is presented to illustrate the calculation of hand exposure for a specific scenario. The dART model is not yet implemented in the ART software platform, and a robust validation of the model is necessary to determine its predictive ability. With advancing knowledge on dermal exposure and its determinants, this model will require periodic updates and refinements, in addition to further expansion of the applicability domain of the model.

Evaluation of Exposure Assessment Tools under REACH: Part II-Higher Tier Tools.

Stoffenmanager®v4.5 and Advanced REACH Tool (ART) v1.5, two higher tier exposure assessment tools for use under REACH, were evaluated by determining accuracy and robustness. A total of 282 exposure measurements from 51 exposure situations (ESs) were collected and categorized by exposure category. In this study, only the results of liquids with vapor pressure (VP) > 10 Pa category having a sufficient number of exposure measurements (n = 251 with 42 ESs) were utilized. In addition, the results were presented by handling/activity description and input parameters for the same exposure category. It should be noted that the performance results of Stoffenmanager and ART in this study cannot be directly compared for some ESs because ART allows a combination of up to four subtasks (and nonexposed periods) to be included, whereas the database for Stoffenmanager, separately developed under the permission of the legal owner of Stoffenmanager, permits the use of only one task to predict exposure estimates. Thus, it would be most appropriate to compare full-shift measurements against ART predictions (full shift including nonexposed periods) and task-based measurements against task-based Stoffenmanager predictions. For liquids with VP > 10 Pa category, Stoffenmanager®v4.5 appeared to be reasonably accurate and robust when predicting exposures [percentage of measurements exceeding the tool's 90th percentile estimate (%M > T) was 15%]. Areas that could potentially be improved include ESs involving the task of handling of liquids on large surfaces or large work pieces, allocation of high and medium VP inputs, and absence of local exhaust ventilation input. Although the ART's median predictions appeared to be reasonably accurate for liquids with VP > 10 Pa, the %M > T for the 90th percentile estimates was 41%, indicating that variance in exposure levels is underestimated by ART. The %M > T using the estimates of the upper value of 90% confidence interval (CI) of the 90th percentile estimate (UCI90) was considerably reduced to 18% for liquids with VP > 10 Pa. On the basis of this observation, users might be to consider using the upper limit value of 90% CI of the 90th percentile estimate for predicting reasonable worst case situations. Nevertheless, for some activities and input parameters, ART still shows areas to be improved. Hence, it is suggested that ART developers review the assumptions in relation to exposure variability within the tool, toward improving the tool performance in estimating percentile exposure levels. In addition, for both tools, only some handling/activity descriptions and input parameters were considered. Thus, further validation studies are still necessary.

Extension of the Advanced REACH Tool (ART) to Include Welding Fume Exposure.

The Advanced REACH Tool (ART) is a mechanistic higher tier model to estimate inhalation exposure to chemicals using a Bayesian approach. Currently the ART model does not include exposure to welding fumes within its applicability domain; it has only been calibrated for vapours, mists, and dusts. To extend the scope to metal fumes it is necessary to review the model structure to ensure that it is appropriate, and to calibrate the updated model using available welding fume exposure measurements. This paper provides a discussion of the key modifying factors (MFs) that should be considered to extend the ART model to include welding fume exposure. Based on our literature review, welding process type, input power level, shield gas, and welding electrodes have important impact on fume formation rates (FFRs). In addition, the convective dispersion of the fume away from the weld and the interaction of the welder with the fume plume should be incorporated into the ART model. Other aspects of the ART, such as the local ventilation, do not require modification to accommodate welding fume exposure. The ART does not include the impact of wearing personal protective equipment and so this is not included in our evaluation. Proposals are made for extending the scope of the ART to include welding processes.

Comparison of the near field/far field model and the advanced reach tool (ART) model V1.5: exposure estimates to benzene during parts washing with mineral spirits.

The Advanced Reach Tool V1.5 (ART) is a mathematical model for occupational exposures conceptually based on, but implemented differently than, the "classic" Near Field/Far Field (NF/FF) exposure model. The NF/FF model conceptualizes two distinct exposure "zones"; the near field, within approximately 1m of the breathing zone, and the far field, consisting of the rest of the room in which the exposure occurs. ART has been reported to provide "realistic and reasonable worst case" estimates of the exposure distribution. In this study, benzene exposure during the use of a metal parts washer was modeled using ART V1.5, and compared to actual measured workers samples and to NF/FF model results from three previous studies. Next, the exposure concentrations expected to be exceeded 25%, 10% and 5% of the time for the exposure scenario were calculated using ART. Lastly, ART exposure estimates were compared with and without Bayesian adjustment. The modeled parts washing benzene exposure scenario included distinct tasks, e.g. spraying, brushing, rinsing and soaking/drying. Because ART can directly incorporate specific types of tasks that are part of the exposure scenario, the present analysis identified each task's determinants of exposure and performance time, thus extending the work of the previous three studies where the process of parts washing was modeled as one event. The ART 50th percentile exposure estimate for benzene (0.425ppm) more closely approximated the reported measured mean value of 0.50ppm than the NF/FF model estimates of 0.33ppm, 0.070ppm or 0.2ppm obtained from other modeling studies of this exposure scenario. The ART model with the Bayesian analysis provided the closest estimate to the measured value (0.50ppm). ART (with Bayesian adjustment) was then used to assess the 75th, the 90th and 95th percentile exposures, predicting that on randomly selected days during this parts washing exposure scenario, 25% of the benzene exposures would be above 0.70ppm; 10% above 0.95ppm; and 5% above 1.15ppm. These exposure estimates at the three different percentiles of the ART exposure distribution refer to the modeled exposure scenario not a specific workplace or worker. This study provides a detailed comparison of modeling tools currently available to occupational hygienists and other exposure assessors. Possible applications are considered.

A Study of the Validity of Two Exposure Assessment Tools: Stoffenmanager and the Advanced REACH Tool.

The use of exposure modelling tools for estimating chemical airborne exposure has increased since the European Union's REACH legislation for safe use of industrial chemicals came into force. Two tools that European Chemicals Agency recommends are Stoffenmanager® and the Advanced REACH Tool (ART). The aim of this study was to investigate the validity of these two exposure modelling tools by comparing the lack of agreement between estimated and measured exposure. We examined the airborne chemical exposure at companies in seven different types of industries: wood, printing, foundry, spray painting, flour milling, chemical industry, and plastic moulding industry. The inhalable exposure of liquids or powders at two to three situations at each company was modelled with both tools and measured. To study the validity of the tools, the mean differences and precisions (lack of agreement) of exposures from both situations handling liquids and powders were calculated by using the 50th percentile outcome of the tools and the geometric mean of the measured exposure (all data were ln transformed). For Stoffenmanager, the mean difference and precision of the situations concerning liquids were 0.22 ± 1.0 and for powders -0.024 ± 0.66. It was also shown that Stoffenmanager overestimated low exposures and underestimated high exposures. Stoffenmanager showed higher agreement with the measured exposure in the wood and flour mill industries than in foundry and the plastic moulding industry. For ART, the mean difference and precision of liquids were -0.55 ± 0.88 and for powders -1.4 ± 1.6. ART showed lower agreement with the measured exposure in the wood industry.

Accuracy Evaluation of Three Modelling Tools for Occupational Exposure Assessment.

The objective of this study is to evaluate the accuracy and robustness of three exposure-modelling tools [STOFFENMANAGER® v.6, European Centre for Ecotoxicology and Toxicology of Chemical Target Risk Assessment v.3.1 (ECETOC TRA v.3.1), and Advanced REACH Tool (ART v.1.5)], by comparing available measured data for exposure to organic solvents and pesticides in occupational exposure scenarios (ESs). Model accuracy was evaluated by comparing the predicted and the measured values, expressed as an underestimation or overestimation factor (PRED/EXP), and by regression analysis. Robustness was quantitatively described by the so-called variable 'Uncertainty Factor' (UF), which was attributed to each model's input: a higher UF score indicates greater model uncertainty and poorer robustness. ART was the most accurate model, with median PRED/EXP factors of 1.3 and 0.15 for organic solvent and pesticide ESs, respectively, and a significant correlation (P < 0.05) among estimated and measured data. As expected, Tier 1 model ECETOC TRA demonstrated the worst performance in terms of accuracy, with median PRED/EXP factors of 2.0 for organic solvent ESs and 3545 for pesticide ESs. Simultaneously, STOFFENMANAGER® showed a median UF equal to 2.0, resulting in the most robust model. ECETOC TRA was not considered acceptable in terms of accuracy, confirming that this model is not appropriate for the evaluation of the selected ESs for pesticides. Conversely, STOFFENMANAGER® was the best choice, and ART tended to underestimate the exposure to pesticides. For organic solvent ESs, there were no cases of strong underestimation, and all models presented overall acceptable results; for the selected ESs, ART showed the best accuracy. Stoffenmanager was the most robust model overall, indicating that even with a mistake in ES interpretation, predicted values would remain acceptable. ART may lead to more accurate results when well-documented ESs are available. In other situations, Stoffenmanager appears to be a safer alternative because of its greater robustness, particularly when entry data uncertainty is difficult to assess. ECETOC TRA cannot be directly compared to higher tiered models because of its simplistic nature: the use of this tool should be limited only to exceptional cases in which a strong conservative and worst-case evaluation is necessary.

Advanced REACH Tool: A Bayesian Model for Occupational Exposure Assessment

This paper describes a Bayesian model for the assessment of inhalation exposures in an occupational setting; the methodology underpins a freely available web-based application for exposure assessment, the Advanced REACH Tool (ART). The ART is a higher tier exposure tool that combines disparate sources of information within a Bayesian statistical framework. The information is obtained from expert knowledge expressed in a calibrated mechanistic model of exposure assessment, data on inter- and intra-individual variability in exposures from the literature, and context-specific exposure measurements. The ART provides central estimates and credible intervals for different percentiles of the exposure distribution, for full-shift and long-term average exposures. The ART can produce exposure estimates in the absence of measurements, but the precision of the estimates improves as more data become available. The methodology presented in this paper is able to utilize partially analogous data, a novel approach designed to make efficient use of a sparsely populated measurement database although some additional research is still required before practical implementation. The methodology is demonstrated using two worked examples: an exposure to copper pyrithione in the spraying of antifouling paints and an exposure to ethyl acetate in shoe repair.

Reliability of the Advanced REACH Tool (ART)

The aim of this study was to assess the reliability of the Advanced REACH Tool (ART) by (i) studying interassessor agreement of the resulting exposure estimates generated by the ART mechanistic model, (ii) studying interassessor agreement per model parameters of the ART mechanistic model, (iii) investigating assessor characteristics resulting in reliable estimates, and (iv) estimating the effect of training on assessor agreement. Prior to the 1-day workshop, participants had to assess four scenarios with the ART. During two 1-day workshops, 54 participants received 3-h training in applying the mechanistic model and the technical aspects of the web tool. Afterward, the participants assessed another four scenarios. The assessments of the participants were compared with gold standard estimates compiled by the workshop instructors. Intraclass correlation coefficients (ICCs) were calculated and per model parameter and the percentage agreement and Cohen kappa statistics were estimated. The ICCs showed good agreement before and almost perfect agreement after training. However, substantial variability was observed between individual assessors' estimates for an individual scenario. After training, only 42% of the assessments lay within a factor of three of the gold standard estimate. The reliability appeared to be influenced by several factors: (i) information provided by text and video hampered the assessors gaining additional information required to make the assessments, (ii) for some parameters, the guidance documentation implemented in the tool may have been insufficient, and (iii) in some cases, the assessors were not able to implement the information explicitly provided. The ART is an expert tool and extensive training is recommended prior to use. Improvements of the guidance documentation, consensus procedures, and improving the training methods could improve the reliability of ART. Nevertheless, considerable variability can be expected between assessors using ART to estimate exposure levels for a given scenario.

The Advanced REACH Tool (ART): Incorporation of an Exposure Measurement Database

This article describes the structure, functionalities, and content of the Advanced REACH Tool (ART) exposure database (version 1.5). The incorporation of the exposure database into ART allows users who do not have their own measurement data for their exposure scenario, to update the exposure estimates produced by the mechanistic model using analogous measurement series selected from the ART exposure measurement database. Depending on user input for substance category and activity (sub)classes, the system selects exposure measurement series from the exposure database. The comprehensive scenario descriptions and summary statistics assist the user in deciding if the measurement series are indeed fully analogous. After selecting one or more analogous data sets, the data are used by the Bayesian module of the ART system to update the mechanistically modeled exposure estimates. The 1944 exposure measurements currently stored in the ART exposure measurement database cover 9 exposure situations for handling solid objects (n = 65), 42 situations for handling powders, granules, or pelletized material (n = 488), 5 situations for handling low-volatility liquids (n = 88), 35 situations for handling volatile liquids (n = 870), and 26 situations for handling liquids in which powders are dissolved or dispersed (resulting in exposure to mist) (n = 433). These 117 measurement series form a good basis for supporting user exposure estimates. However, by increasing the diversity of exposure situations and the number of measurement series in the database, the usefulness of the ART system will be further improved. Suggestions to stimulate the process of sharing exposure measurement data both to increase the available data in the ART and for other purposes are made.