Review of generic scenario environmental release and occupational exposure models used in chemical risk assessment

Under the Toxic Substances Control Act (TSCA), the United States Environmental Protection Agency (USEPA) is required to determine whether a new chemical substance poses an unreasonable risk to human health or the environment before the chemical is manufactured in or imported into the United States. This manuscript provides a review of the process used to evaluate the risk associated with a chemical based on the scenarios and models used in the evaluation. Specifically, the Generic Scenarios and Emission Scenario Documents developed by the USEPA were reviewed, along with background documentation prepared by USEPA to identify the core elements of the environmental release and occupational exposure scenarios used to assess the risk of the chemical being evaluated. Additionally, this contribution provides an overview of methods used to model occupational exposures and environmental releases as part of the chemical evaluation process used in other jurisdictions, along with work being performed to improve these models. Finally, the alternative methods to evaluate occupational exposures and environmental releases that may be used as part of the decision-making process regarding a chemical are identified. The contribution provides a path forward for reducing the time required and improving the chemical evaluation of the unreasonable risk determination regarding the manufacture or import of a chemical.

Understanding the limitations and application of occupational exposure models in a REACH context

Exposure modeling plays a significant role for regulatory organizations, companies, and professionals involved in assessing and managing occupational health risks in workplaces. One context in which occupational exposure models are particularly relevant is the REACH Regulation in the European Union (Regulation (EC) No 1907/2006). This commentary describes the models for the occupational inhalation exposure assessment of chemicals within the REACH framework, their theoretical background, applications, and limitations, as well as the latest developments and priorities for model improvement. Summing up the debate, despite its relevance and importance in the context of REACH not being in question, occupational exposure modeling needs to be improved in many respects. There is a need to reach a wide consensus on several key issues (e.g., the theoretical background and the reliability of modeling tools), to consolidate and monitor model performance and regulatory acceptance, and to align practices and policies regarding exposure modeling.

Evaluation of One- and Two-Box Models as Particle Exposure Prediction Tools at Industrial Scale

One- and two-box models have been pointed out as useful tools for modelling indoor particle exposure. However, model performance still needs further testing if they are to be implemented as trustworthy tools for exposure assessment. The objective of this work is to evaluate the performance, applicability and reproducibility of one- and two-box models on real-world industrial scenarios. A study on filling of seven materials in three filling lines with different levels of energy and mitigation strategies was used. Inhalable and respirable mass concentrations were calculated with one- and two-box models. The continuous drop and rotating drum methods were used for emission rate calculation, and ranges from a one-at-a-time methodology were applied for local exhaust ventilation efficiency and inter-zonal air flows. When using both dustiness methods, large differences were observed for modelled inhalable concentrations but not for respirable, which showed the importance to study the linkage between dustiness and processes. Higher model accuracy (ratio modelled vs. measured concentrations 0.5-5) was obtained for the two- (87%) than the one-box model (53%). Large effects on modelled concentrations were seen when local exhausts ventilation and inter-zonal variations where parametrized in the models. However, a certain degree of variation (10-20%) seems acceptable, as similar conclusions are reached.

Modelling Exposure by Spraying Activities-Status and Future Needs

Spray applications enable a uniform distribution of substances on surfaces in a highly efficient manner, and thus can be found at workplaces as well as in consumer environments. A systematic literature review on modelling exposure by spraying activities has been conducted and status and further needs have been discussed with experts at a symposium. This review summarizes the current knowledge about models and their level of conservatism and accuracy. We found that extraction of relevant information on model performance for spraying from published studies and interpretation of model accuracy proved to be challenging, as the studies often accounted for only a small part of potential spray applications. To achieve a better quality of exposure estimates in the future, more systematic evaluation of models is beneficial, taking into account a representative variety of spray equipment and application patterns. Model predictions could be improved by more accurate consideration of variation in spray equipment. Inter-model harmonization with regard to spray input parameters and appropriate grouping of spray exposure situations is recommended. From a user perspective, a platform or database with information on different spraying equipment and techniques and agreed standard parameters for specific spraying scenarios from different regulations may be useful.

Modeling occupational exposure to solvent vapors using the Two-Zone (near-field/far-field) model: a literature review

The Two-Zone model is used in occupational hygiene to predict both near-field and far-field airborne contaminant concentrations. A literature review was carried out on 21 scientific publications in which the Two-Zone model was used to assess occupational exposure to solvent vapors. Data on exposure scenarios, solvents, generation/emission rates, near- and far-field parameters, and model performance were collected and analyzed. Over the 24 exposure scenarios identified, 18 were evaluated under controlled conditions, 5 under normal workplace activities, and 1 was reported based on literature data. The scenarios involved a variety of tasks which consisted, mostly, of cleaning metal parts, spraying solvents onto surfaces, spilling liquids, and filling containers with volatile substances. Twenty-eight different solvents were modeled and the most commonly tested were benzene, toluene, and acetone. Emission rates were considered constant in 16 scenarios, exponentially decreasing in 6 scenarios, and intermittent in 2 scenarios. Four-hundred-and-forty-six (446) predicted-to-measured concentration ratios were calculated across the 21 studies; 441 were obtained in controlled conditions, 4 under normal workplace activities, and 1 was calculated based on the literature data. For controlled studies, the Two-Zone model predictive performance was within a factor of 0.3-3.7 times the measured concentrations with 93% of the values between 0.5 and 2. The model overestimated the measured concentrations in 63% of the evaluations. The median predicted concentration for the near-field was 1.38 vs. 1.02 for the far-field. Results suggest that the model might be a useful tool for predicting occupational exposure to vapors of solvents by providing a conservative approach. Harmonization in model testing strategies and data presentation is needed in future studies to improve the assessment of the predictability of the Two-Zone model. Moreover, this review has provided a database of exposure scenarios, input parameter values, and model predictive performances which can be useful to occupational hygienists in their future modeling activities.

Occupational Exposure and Environmental Release: The Case Study of Pouring TiO2 and Filler Materials for Paint Production

Pulmonary exposure to micro- and nanoscaled particles has been widely linked to adverse health effects and high concentrations of respirable particles are expected to occur within and around many industrial settings. In this study, a field-measurement campaign was performed at an industrial manufacturer, during the production of paints. Spatial and personal measurements were conducted and results were used to estimate the mass flows in the facility and the airborne particle release to the outdoor environment. Airborne particle number concentration (1 × 10³–1.0 × 10⁴ cm⁻³), respirable mass (0.06–0.6 mg m⁻³), and PM₁₀ (0.3–6.5 mg m⁻³) were measured during pouring activities. In overall; emissions from pouring activities were found to be dominated by coarser particles >300 nm. Even though the raw materials were not identified as nanomaterials by the manufacturers, handling of TiO₂ and clays resulted in release of nanometric particles to both workplace air and outdoor environment, which was confirmed by TEM analysis of indoor and stack emission samples. During the measurement period, none of the existing exposure limits in force were exceeded. Particle release to the outdoor environment varied from 6 to 20 g ton⁻¹ at concentrations between 0.6 and 9.7 mg m⁻³ of total suspended dust depending on the powder. The estimated release of TiO₂ to outdoors was 0.9 kg per year. Particle release to the environment is not expected to cause any major impact due to atmospheric dilution

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.

Evaluation of Stoffenmanager and a New Exposure Model for Estimating Occupational Exposure to Styrene in the Fiberglass Reinforced Plastics Lamination Process

This study aims to evaluate occupational exposure models by comparing model estimations of Stoffenmanager, version 8.2, and exposure scores calculated using a new exposure model with personal exposure measurements for styrene used in the fiberglass-reinforced plastic (FRP) lamination processes in Korea. Using the collected exposure measurements (n = 160) with detailed contextual information about the type of process, working conditions, local exhaust ventilation, respiratory protections, and task descriptions, we developed a new model algorithm to estimate the score for occupational exposures on situation level. We assumed that the source of exposure originates from the near field only (within the breathing zone of workers). The new model is designed as a simple formula of multiplying scores for job classification, exposure potential, engineering controls, chemical hazard, and exposure probability and then dividing the score for workplace size. The final score is log-transformed, ranging from 1 to 14, and the exposure category is divided into four ratings: no exposure (1), low (2), medium (3), and high (4) exposures. Using the contextual information, all the parameters and modifying factors are similarly entered into the two models through direct translation and coding processes with expert judgement, and the exposure estimations and scores using the two models are calculated for each situation. Overall bias and precision for Stoffenmanager are −1.00 ± 2.07 (50th) and −0.32 ± 2.32 (90th) for all situations (n = 36), indicating that Stoffenmanager slightly underestimated styrene exposures. Pearson’s correlation coefficients are significantly high for Stoffenmanager (r = 0.87) and the new model (r = 0.88), and the correlation between the two models is significantly high (r = 0.93) (p < 0.01). Therefore, the model estimations using Stoffenmanager and the new model are significantly correlated with the styrene exposures in the FRP lamination process. Further studies are needed to validate and calibrate the models using a larger number of exposure measurements for various substances in the future.

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.

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.

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.

Health Risk Assessment of Photoresists Used in an Optoelectronic Semiconductor Factory

Photoresist materials are indispensable in photolithography, a process used in semiconductor fabrication. The work process and potential hazards in semiconductor production have raised concerns as to adverse health effects. We therefore performed a health risk assessment of occupational exposure to positive photoresists in a single optoelectronic semiconductor factory in Taiwan. Positive photoresists are widely used in the optoelectronic semiconductor industry for photolithography. Occupational exposure was estimated using the Stoffenmanager® model. Bayesian modeling incorporated available personal air sampling data. We examined the composition and by-products of the photoresists according to descriptions published in the literature and patents; the main compositions assessed were propylene glycol methyl ether acetate (PGMEA), novolac resin, photoactive compound, phenol, cresol, benzene, toluene, and xylene. Reference concentrations for each compound were reassessed and updated if necessary. Calculated hazard quotients were greater than 1 for benzene, phenol, xylene, and PGMEA, indicating that they have the potential for exposures that exceed reference levels. The information from our health risk assessment suggests that benzene and phenol have a higher level of risk than is currently acknowledged. Undertaking our form of risk assessment in the workplace design phase could identify compounds of major concern, allow for the early implementation of control measures and monitoring strategies, and thereby reduce the level of exposure to health risks that workers face throughout their career.

How to Obtain a Reliable Estimate of Occupational Exposure? Review and Discussion of Models' Reliability

Evaluation and validation studies of quantitative exposure models for occupational exposure assessment are still scarce and generally only consider a limited number of exposure scenarios. The aim of this review was to report the current state of knowledge of models’ reliability in terms of precision, accuracy, and robustness. A systematic review was performed through searches of major scientific databases (Web of Science, Scopus, and PubMed), concerning reliability of Tier1 (“ECETOC TRA”-European Centre for Ecotoxicology and Toxicology of Chemicals Targeted Risk Assessment, MEASE, and EMKG-Expo-Tool) and Tier2 models (STOFFENMANAGER^® and “ART”-Advanced Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) Tool). Forty-five studies were identified, and we report the complete information concerning model performance in different exposure scenarios, as well as between-user reliability. Different studies describe the ECETOC TRA model as insufficient conservative to be a Tier1 model, in different exposure scenarios. Contrariwise, MEASE and EMKG-Expo-Tool seem to be conservative enough, even if these models have not been deeply evaluated. STOFFENMANAGER^® resulted the most balanced and robust model. Finally, ART was generally found to be the most accurate and precise model, with a medium level of conservatism. Overall, the results showed that no complete evaluation of the models has been conducted, suggesting the need for correct and harmonized validation of these tools.

On the Strength and Validity of Hazard Banding

Hazard Banding (HB) is a process of allocating chemical substances in bands of increasing health hazard based on their hazard classifications. Recent Control Banding (CB) tools use the classifications of the United Nations Global Harmonized System (UN GHS) or the European Union Classifications, Labelling and Packaging (EU CLP) which are grouped over 5 HBs. The use of CB is growing worldwide for the risk control of substances without an Occupational Exposure Limit Value (OELV). Well-known CB-tools like HSE-COSHH Essentials, BAuA-Einfaches Maßnahmenkonzept Gefahrstoffe (EMKG), and DGUV-IFA-Spaltenmodell (IFA) use however different GHS/CLP groupings which may lead to dissimilar HBs and control regimes for individual substances. And as the choice for a CB tool seems to be determined by geography and/or local status these differences may hamper a global, aligned HSE approach. Therefore, the HB-engines of the three public CBs and an in-company (Solvay) CB called ‘Occupational Exposure Banding’ (S-OEB) were compared mutually and ranked in their relation with the OELV as the ‘de facto’ standard. This was investigated graphically and using a 5 strength indicator, statistical method. A data set of 229 substances with high-quality GHS/CLP classifications and OELVs was used. HB concentration ranges, as linked to S-OEB and COSHH, were validated against the corresponding OELV distributions. The four HB-engines allocate between 23 and 64% of the 229 substances in the same bands. The remaining substances differ at least one band, with IFA placing more substances in a higher hazard band, EMKG doing the opposite and COSHH and S-OEB in between. The overall strength scores of S-OEB, IFA, and EMGK HB-engines are higher than COSHH, with S-OEB having the highest overall strength score. The lower ends of the concentration ranges defined for the 3 ‘highest’ hazard bands of S-OEB were in good agreement with the 10^th percentiles of the corresponding OELV distributions obtained from the substance data set. The lower ends of the COSHH concentration ranges comply with the 10^th percentiles of the COSHH OELV distributions for dust/aerosol but not for vapour/gas substances. Both the S-OEB and COSHH concentration ranges underestimate the overall width of the OELV distributions that can span 2–3 orders of magnitude. As the performance of the S-OEB HB-engine meets our criteria of being at least as good as the public engines, it will be used as a standard within Solvay’s global operations. In addition, the method described here to evaluate the strength of HB-engines and the validity of their corresponding concentration ranges is a useful tool enabling further developments and worldwide alignment of HB.

Application of predictive models for estimation of health care workers exposure to sevoflurane

OBJECTIVES

The aim of this study was to assess the potential use of predictive models to estimate professional exposure to chemicals in the workplace, such as the operating room, by simultaneous determination of the levels of exposure using a model and measurements.

METHODS

Measurements included determinations of sevoflurane (SEV) in the workplace air of 117 operating rooms of 31 hospitals in one Polish region. Measurements were carried out at the time of various surgical procedures during administration of anaesthetics by endotracheal intubation. The measurement results were compared with the values estimated using two models: ECETOC TRA and Stoffenmanager.

RESULTS

In one case the ECETOC TRA estimated the exposure concentration almost equal to the measured concentration but, because of the need to maintain a margin of safety in case of modelling, it can be concluded that the model underestimated the concentration. The Stoffenmanager model provided accurate exposure estimates in the examined case, and it can be used as a screening tool for the assessment of occupational inhalation exposure of medical personnel to anaesthetics.

CONCLUSIONS

The results are of particular importance to the circumstances in Eastern Europe, where the levels of anaesthetics often exceed the relevant occupational exposure limits.

Sensitivity analysis, dominant factors, and robustness of the ECETOC TRA v3, Stoffenmanager 4.5, and ART 1.5 occupational exposure models

Occupational exposure modeling is widely used in the context of the E.U. regulation on the registration, evaluation, authorization, and restriction of chemicals (REACH). First tier tools, such as European Centre for Ecotoxicology and TOxicology of Chemicals (ECETOC) targeted risk assessment (TRA) or Stoffenmanager, are used to screen a wide range of substances. Those of concern are investigated further using second tier tools, e.g., Advanced REACH Tool (ART). Local sensitivity analysis (SA) methods are used here to determine dominant factors for three models commonly used within the REACH framework: ECETOC TRA v3, Stoffenmanager 4.5, and ART 1.5. Based on the results of the SA, the robustness of the models is assessed. For ECETOC, the process category (PROC) is the most important factor. A failure to identify the correct PROC has severe consequences for the exposure estimate. Stoffenmanager is the most balanced model and decision making uncertainties in one modifying factor are less severe in Stoffenmanager. ART requires a careful evaluation of the decisions in the source compartment since it constitutes ∼75% of the total exposure range, which corresponds to an exposure estimate of 20-22 orders of magnitude. Our results indicate that there is a trade off between accuracy and precision of the models. Previous studies suggested that ART may lead to more accurate results in well-documented exposure situations. However, the choice of the adequate model should ultimately be determined by the quality of the available exposure data: if the practitioner is uncertain concerning two or more decisions in the entry parameters, Stoffenmanager may be more robust than ART.

Bayesian hierarchical framework for occupational hygiene decision making

A hierarchical Bayesian framework has been developed for exposure assessment that makes use of statistical sampling-based techniques to estimate the posterior probability of the 95th percentile or arithmetic mean of the exposure distribution being located in one of several exposure categories. The framework can synthesize professional judgment and monitoring data to yield an updated posterior exposure assignment for routine exposure management. The framework is versatile enough that it can be modified for use in epidemiological studies for classifying the arithmetic mean instead of the 95th percentile into several exposure categories. Various physico-chemical exposure models have also been incorporated in the hierarchical framework. The use of the framework in three settings has been illustrated. First, subjective judgments about exposure magnitude obtained from industrial hygienists for five tasks were treated as priors in the Bayesian framework. Monitoring data for each task were used to create a likelihood function in the hierarchical framework and the posterior was predicted in terms of the 95th percentile being located in each of the four AIHA exposure categories. The accuracy of the exposure judgments was then evaluated. Second, we illustrate the use of exposure models to develop priors in this framework and compare with monitoring data in an iron foundry. Finally, we illustrate the use of this approach for retrospective exposure assessment in a chemical manufacturing facility, to categorize exposures based on arithmetic mean instead of 95th percentile.

Exposure assessment of four pharmaceutical powders based on dustiness and evaluation of damaged HEPA filters

In this study, we show the different dustiness characteristics of four molecular pharmaceutical powder candidates and evaluate the performance of HEPA filters damaged with three different pinhole sizes and exposed to dust using real industrial powders in a miniaturized EN15051 rotating drum dustiness tester. We then demonstrate the potential use of such data using first-order exposure modeling to assess the potential worker exposure and transmission of active powder ingredients into ventilation systems. The four powders had highly variable inhalable dustiness indices (1,036 - 14,501 mg/kg). Dust particle size-distributions were characterized by three peaks; the first occurred around 60-80 nm, the second around 250 nm, and the third at 2-3 μm. The second and third peaks are often observed in dustiness test studies, but peaks in the 60-80 nm range have not been previously reported. Exposure modeling in a 5 times 20 kg powder pouring scenario, suggests that excessive dust concentrations may be reached during use of powders with the highest dustiness levels. By number, filter-damage by three pinhole sizes resulted in damage-dependent penetration of 70-80 nm-size particles, but by volume and mass the penetration is still dominated by particles larger than 100 nm. Whereas the exposure potential was evident, the potential dust concentrations in air ducts following the pouring scenario above were at pg/m(3) levels. Hence, filter penetration at these damage levels was assumed to be only critical, if the active ingredients were associated with high hazard or unique product purity is required. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource: An example of a typical particle number time-series of a complete dustiness test. It provides information on the HEPA-filter used including a scanning electron microscopy image of it. It also provides APS-measurements of particles penetrating the damaged HEPA-filter.].

Banding the world together; the global growth of control banding and qualitative occupational risk management

Control Banding (CB) strategies to prevent work-related illness and injury for 2.5 billion workers without access to health and safety professionals has grown exponentially this last decade. CB originates from the pharmaceutical industry to control active pharmaceutical ingredients without a complete toxicological basis and therefore no occupational exposure limits. CB applications have broadened into chemicals in general - including new emerging risks like nanomaterials and recently into ergonomics and injury prevention. CB is an action-oriented qualitative risk assessment strategy offering solutions and control measures to users through "toolkits". Chemical CB toolkits are user-friendly approaches used to achieve workplace controls in the absence of firm toxicological and quantitative exposure information. The model (technical) validation of these toolkits is well described, however firm operational analyses (implementation aspects) are lacking. Consequentially, it is often not known if toolkit use leads to successful interventions at individual workplaces. This might lead to virtual safe workplaces without knowing if workers are truly protected. Upcoming international strategies from the World Health Organization Collaborating Centers request assistance in developing and evaluating action-oriented procedures for workplace risk assessment and control. It is expected that to fulfill this strategy's goals, CB approaches will continue its important growth in protecting workers.

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.

Classification of occupational activities for assessment of inhalation exposure

There is a large variety of activities in workplaces that can lead to emission of substances. Coding systems based on determinants of emission have so far not been developed. In this paper, a system of Activity Classes and Activity Subclasses is proposed for categorizing activities involving chemical use. Activity Classes share their so-called 'emission generation mechanisms' and physical state of the product handled and the underlying determinants of emission. A number of (industrial) stakeholders actively participated in testing and fine-tuning the system. With the help of these stakeholders, it was found to be relatively easy to allocate a large number of activities to the Activity Classes and Activity Subclasses. The system facilitates a more structured classification of activities in exposure databases, a structured analysis of the analogy of exposure activities, and a transparent quantification of the activity emission potential in (new) exposure assessment models. The first use of the system is in the Advanced REACH Tool.

Refinement and validation of an exposure model for the pharmaceutical industry

OBJECTIVES

Assessment of worker's exposure is becoming increasingly critical in the pharmaceutical industry as drugs of higher potency are being manufactured. The batch nature of operations often makes it difficult to obtain sufficient numbers of exposure measurements and occupational exposure models may be useful tools in the exposure assessment process. This paper aims to describe further refinement and validation of an existing deterministic occupational exposure model to predict airborne exposure of workers in this industry.

METHODS

Workplace exposure assessment data (n = 381) containing all the contextual information required for the exposure model were collated from a multinational pharmaceutical company. The measured exposure levels ranged from 5 × 10⁻⁷ to 200 mg m⁻³ for largely task based samples, and included a range of handling activities, local control measures and abnormal operating conditions. Model input parameters for local control measures and handling activities were refined to reflect pharmaceutical situations.

RESULTS

The refined exposure model resulted in good correlations between the log-transformed model predictions and the actual measured data for the overall dataset (r(s) = 0.61, n = 381, p < 0.001) and at scenario level (r(s) = 0.69, n = 48, p < 0.001). The model overestimated scenarios with measured exposure levels < 0.1 mg m⁻³ (r(s) = 0.69, bias = 0.71, n = 46, p < 0.001), and underestimated scenarios with higher measured concentrations ( > 0.1 mg m⁻³) (r(s) = 0.59, bias = -4.9, n = 33, p < 0.001). Including information on the refined sub-parameters improved the correlations, suggesting the uncertainty in the model parameters was partly responsible for the bias.

CONCLUSION

Further scientific data from the pharmaceutical industry on model input parameters, particularly on the efficacy of local control measures, may help improve the accuracy of the model predictions. The refined exposure model appears to be a useful exposure assessment screening tool for the pharmaceutical industry.

Use of read-across and tiered exposure assessment in risk assessment under REACH--a case study on a phase-in substance

REACH requests the exploration of alternative strategies for hazard identification before resorting to (in vivo) testing. Here, we combined read-across as non-testing strategy with a tiered exposure assessment for the risk characterisation of 1-methoxypropan-2-ol (PGME) as a representative for phase-in substances to be registered under REACH. Read-across from the selected source substances provided data which were comparable with experimental data available for target substance PGME, resulting in a realistic starting point for both qualitative and quantitative risk assessment. Greater variability was observed in the exposure estimates from a first Tier model (ECETOC TRA) or less conservative further Tier models (Stoffenmanager; RISKOFDERM), when these results were compared with results from a data-rich approach using measured data. When safe use of chemicals cannot be demonstrated with these approaches, refinement can be introduced in the estimation of hazard and exposure, or both. In view of the variability associated with exposure modeling, it may often add more value to invest in realistic exposure data than in toxicity studies, apart from animal welfare considerations.

Stoffenmanager exposure model: company-specific exposure assessments using a Bayesian methodology

The web-based tool "Stoffenmanager" was initially developed to assist small- and medium-sized enterprises in the Netherlands to make qualitative risk assessments and to provide advice on control at the workplace. The tool uses a mechanistic model to arrive at a "Stoffenmanager score" for exposure. In a recent study it was shown that variability in exposure measurements given a certain Stoffenmanager score is still substantial. This article discusses an extension to the tool that uses a Bayesian methodology for quantitative workplace/scenario-specific exposure assessment. This methodology allows for real exposure data observed in the company of interest to be combined with the prior estimate (based on the Stoffenmanager model). The output of the tool is a company-specific assessment of exposure levels for a scenario for which data is available. The Bayesian approach provides a transparent way of synthesizing different types of information and is especially preferred in situations where available data is sparse, as is often the case in small- and medium sized-enterprises. Real-world examples as well as simulation studies were used to assess how different parameters such as sample size, difference between prior and data, uncertainty in prior, and variance in the data affect the eventual posterior distribution of a Bayesian exposure assessment.

Training health and safety committees to use control banding: lessons learned and opportunities for the United States

Control banding (CB) is a control-focused risk management model that has received international attention. CB strategies are designed to control workplace chemical exposures after the completion of a qualitative risk assessment. Connecticut was one of the first states to provide training on how to use this control-focused tool. Joint labor/management teams and individuals from 34 workplaces attended a control banding workshop and learned how to use one CB model, the United Kingdom (UK) Health and Safety Executive's Control of Substances Hazardous to Health (COSHH) Essentials Toolkit. After the initial training program the investigators used follow-up workshops, questionnaires, site visit data, and case studies to evaluate the training curriculum and assess the utility and effectiveness of this CB strategy. We found that the model is easily learned, although several areas for improvement were identified. Participants from 10 workplaces used COSHH Essentials to evaluate at least one task. The training curriculum was effective in that the agreement between the exposure variables coded by these workplaces and one of the workshop instructors, a certified industrial hygienist (CIH), were highly concordant. The training curriculum and the model promoted a discussion of risk between workers and managers and resulted in the implementation of improvements in the work environment. The model agreed with both the CIH's and the worksites' qualitative risk assessments 65% of the time, and likely over-controlled for 71% (5/7) of the cases of nonagreement. Feedback from workshop participants benefits the current dialogue on the implications of implementing CB in the United States.