Occupational inhalation exposure during surface disinfection-exposure assessment based on exposure models compared with measurement data

BACKGROUND

For healthcare workers, surface disinfections are daily routine tasks. An assessment of the inhalation exposure to hazardous substances, in this case the disinfectant´s active ingredients, is necessary to ensure workers safety. However, deciding which exposure model is best for exposure assessment remains difficult.

OBJECTIVE

The aim of the study was to evaluate the applicability of different exposure models for disinfection of small surfaces in healthcare settings.

METHODS

Measurements of the air concentration of active ingredients in disinfectants (ethanol, formaldehyde, glutaraldehyde, hydrogen peroxide, peroxyacetic acid) together with other exposure parameters were recorded in a test chamber. The measurements were performed using personal and stationary air sampling. In addition, exposure modelling was performed using three deterministic models (unsteady 1-zone, ConsExpo and 2-component) and one modifying-factor model (Stoffenmanager®). Their estimates were compared with the measured values using various methods to assess model quality (like accuracy and level of conservatism).

RESULTS

The deterministic models showed overestimation predominantly in the range of two- to fivefold relative to the measured data and high conservatism for all active ingredients of disinfectants with the exception of ethanol. With Stoffenmanager® an exposure distribution was estimated for ethanol, which was in good accordance with the measured data.

IMPACT STATEMENT

To date, workplace exposure assessments often involve expensive and time consuming air measurements. Reliable exposure models can be used to assess occupational inhalation exposure to hazardous substances, in this case surface disinfectants. This study describes the applicability of three deterministic and one modifying-factor model for disinfection of small surfaces in healthcare settings, in direct comparison to measurements performed and will facilitate future exposure assessments at these workplaces.

Exposure assessment during paint spraying and drying using PTR-ToF-MS

Spraying is a common way to distribute occupational products, but it puts worker's health at risk by exposing them to potentially harmful particles and gases. The objective of this study is to use time-resolved measurements to gain an understanding of spray applications at the process level and to compare them to predictions of exposure models. We used proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) at 1-s time resolution to monitor the gas phase concentration of the solvents acetone, ethanol, butyl acetate, xylene and 1-methoxy-2-propy acetate during outdoor spraying and indoor drying of metal plate under various conditions of outdoor air supply. We found that during spraying, gas-phase exposure was dominated by the more volatile solvents acetone and ethanol, which exhibited strong concentration variations due to the outdoor winds. During drying, exposure strongly depended on the strength of ventilation. Under conditions with high supply of outdoor air, our measurements show a near-exponential decay of the solvent concentrations during drying. Conversely, under conditions without outdoor air supply, the drying process required hours, during which the less volatile solvents passed through a concentration maximum in the gas phase, so that the exposure during drying exceeded the exposure during spraying. The concentrations measured during spraying were then compared for each of the substances individually with the predictions of the exposure models ECETOC TRA, Stoffenmanager, and ART using TREXMO. For these conditions, ECETOC TRA and Stoffenmanager predicted exposures in the measured concentration range, albeit not conservative for all solvents and each application. In contrast, ART largely overestimated the exposure for the more volatile solvents acetone and ethanol and slightly underestimated exposure to 1M2PA for one spraying. ECETOC TRA and ART do not have options to predict exposure during drying. Stoffenmanager has the option to predict drying together with spraying, but not to predict the drying phase independently. Our study demonstrates the importance of considering both the spray cloud and solvent evaporation during the drying process. To improve workplace safety, there is a critical need for enhanced exposure models and comprehensive datasets for calibration and validation covering a broader range of exposure situations.

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 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.

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.

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.

Incorporating Exposure Measurement Data from Similar Exposure Scenarios to Inform Exposure Modeling Estimates: A Demonstration Using Cluster Analysis and Bayesian Modeling

Addressing occupational health and safety concerns early in the design stage anticipates hazards and enables health professionals to recommend control measures that can best protect workers' health. This method is a well-established tool in public health. Importantly, its success depends on a comprehensive exposure assessment that incorporates previous exposure data and outcomes. Traditional methods for characterizing similar occupational exposure scenarios rely on expert judgment or qualitative descriptions of relevant exposure data, which often include undisclosed underlying assumptions about specific exposure conditions. Thus, improved methods for predicting exposure modeling estimates based on available data are needed. This study proposes that cluster analysis can be used to quantify the relevance of existing exposure scenarios that are similar to a new scenario. We demonstrate how this method improves exposure predictions. Exposure data and contextual information of the scenarios were collected from past exposure assessment reports. Prior distributions for the exposure distribution parameters were specified using Stoffenmanager® 8 predictions. Gower distance and k-Medoids clustering algorithm analyses grouped existing scenarios into clusters based on similarity. The information was used in a Bayesian model to specify the degree of correlation between similar scenarios and the scenarios to be assessed. Using the distance metric to characterize the degree of similarity, the performance of the Bayesian model was improved in terms of the average bias of model estimates and measured data, reducing from 0.77 (SD: 2.0) to 0.49 (SD: 1.8). Nevertheless, underestimation of exposures still occurred for some rare scenarios, which tended to be those with highly variable exposure data. In conclusion, the cluster analysis approach may enable transparent selection of similar exposure scenarios for factoring into design-phase assessments and thereby improve exposure modeling estimates.

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.

Nanoparticle Exposure and Workplace Measurements During Processes Related to 3D Printing of a Metal Object

Metal 3D printing has many potential uses within prototyping and manufacturing. Selective laser melting (SLM) is a process that uses metal powders in the micrometer range as printing material. The particle release from the entire SLM printing process is not well-studied. While the 3D printing itself often occurs in a sealed chamber, activities related to the process can potentially release harmful metal particles to the indoor working environment through resuspension of the printing powder or via incident nanoparticles generated during printing. The objective of this study was to improve the understanding of particle exposure in work processes associated with 3D printing and potential needs for interventions by a case study conducted in a 3D printing facility. In this setting, direct release and dispersion of particles throughout the workspace from processes related to metal 3D printing was investigated. The release from five activities were studied in detail. The activities included post-printing cleaning, object annealing, and preparation of new base substrate for the next printing was. Three of the five measured activities caused particles number concentrations in the working environment to increase above background levels which were found to be 8·10² cm^-3. Concentrations during chamber emptying and the open powder removal system (PRS) cleaning processes increased to 10⁴ and 5·10³ cm^-3, respectively, whereas grinding activity increased number concentrations to 2.5·10⁵ cm^-3. Size distributions showed that particles were mainly smaller than 200 nm. Respirable mass concentrations were 50.4 μg m^-3, collected on filters. This was corroborated by respirable mass measured with a DustTrak of 58.4 μg m^-3. Respirable mass concentrations were below the occupational exposure limits in Denmark for an 8 h time-weighted average.

The ECETOC-Targeted Risk Assessment Tool for Worker Exposure Estimation in REACH Registration Dossiers of Chemical Substances-Current Developments

(1) Background: The ECETOC Targeted Risk Assessment (TRA) tool is widely used for estimation of worker exposure levels in the development of dossiers for REACH registration of manufactured or imported chemical substances in Europe. A number of studies have been published since 2010 in which the exposure estimates of the tool are compared with workplace exposure measurement results and in some instances an underestimation of exposure was reported. The quality and results of these studies are being reviewed by ECETOC. (2) Methods: Original exposure measurement data from published comparison studies for which six or more data points were available for each workplace scenario and a TRA estimate had been developed to create a curated database to examine under what conditions and for which applications the tool is valid or may need adaptation. (3) Results: The published studies have been reviewed for completeness and clarity and TRA estimates have been constructed based on the available information, following a set of rules. The full review findings are expected to be available in the course of 2021. (4) Conclusions: The ECETOC TRA tool developers periodically review the validity and limitations of their tool, in line with international recommendations.

Inter-assessor Agreement for TREXMO and Its Models Outside the Translation Framework

Within the framework of Registration, Evaluation, Authorization, and restriction of CHemicals (REACH), occupational exposure models are often used to predict the levels of exposure at a workplace. Poor inter-assessor agreement with their use poses a concern that may lead to different and dangerous risk conclusions. TREXMO (v1, 2016), a web tool providing parameter translations between six exposure models under REACH, is expected to improve the inter-assessor agreement. In this study, 18 assessors performed exposure assessment for six exposure situations within and outside the framework of this translation tool. In more than half of the evaluated cases, the results showed better agreement between assessors selecting the exposure parameters within the framework of TREXMO than when manually coding. The most affected were the parameters related to activity (such as "handling types" of Stoffenmanager) and exposure control (such as local controls). Furthermore, the agreement between the estimates calculated by different assessors was also improved when performing the translations between the models. For Stoffenmanager, for example, the relative standard deviation of 70-121%, obtained for vapors without applying the translation system, was 29-94% with the translations from ART. These findings showed a potential of TREXMO to impact the inter-assessor agreement. Because the study was limited to 18 assessors and only six exposure situations were assessed, further investigations are suggested.

Validity of Tier 1 Modelling Tools and Impacts on Exposure Assessments within REACH Registrations-ETEAM Project, Validation Studies and Consequences

In the last years, the evaluation and validation of exposure modelling tools for inhalation exposure assessment at workplaces received new and highly increased attention by different stakeholders. One important study in this regard is the ETEAM (Evaluation of Tier 1 Exposure Assessment Models) project that evaluated exposure assessment tools under the European REACH regulation (Registration, Evaluation, Authorisation and Restriction of Chemicals), (but next to the ETEAM project-as a project publicly funded by the German Federal Institute for Occupational Safety and Health (BAuA)-it is a rather new development that research groups from universities in Europe, but also internationally, investigated this issue. These other studies focused not only on REACH tier 1 tools but also investigated other tools and aspects of tool validity. This paper tries to summarise the major findings of studies that explored the different issues of tool validity by focusing on the scientific outcomes and the exposure on the science community. On the other hand, this publication aims to provide guidance on the choice and use of tools, addressing the needs of tool users. The consequences of different stakeholders under REACH are discussed from the results of the validation studies. The major stakeholders are: (1) REACH registrants or applicants for REACH authorisations, meaning those companies, consortia or associations who are subject to REACH; (2) Evaluating authorities within the scope of REACH, meaning the ECHA (European Chemicals Agency) secretariat and committees, but also the competent authorities of the member states or the European Union; (3) Developers of the different models and tools; (4) Users of the different models and tools.

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.

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.

Evaluating the Risk Assessment Approach of the REACH Legislation: A Case Study

Risk assessments based on occupational exposure to chemicals have increased since REACH (European regulation on Registration, Evaluation, Authorization, and restriction of Chemicals) came into force. The European Chemicals Agency (ECHA) recommends that chemical exposure could be calculated using exposure models and that parameters used to calculate the exposure scenario (ES) should be communicated in extended safety data sheets (e-SDS) as workplace instructions which downstream users are obligated to follow. We aimed to evaluate REACH's risk assessment approach using the Stoffenmanager® 6.1, the Advanced REACH Tool 1.5 (ART), and the European Centre for Ecotoxicology and Toxicology of Chemicals' targeted risk assessment (ECETOC TRA 3.1) exposure models. We observed 239 scenarios in three companies handling chemicals using 45 e-SDS. Risk characterization ratios (RCRs) were calculated by dividing estimated exposures by derived no-effect levels (DNELs). Observed RCRs were much lower than registered RCRs, indicating lower exposures. However, about 12% of the observed ES still had RCRs > 1, after adjustment for control measures and personal protections described in the ES, when using Stoffenmanager®. The ES with observed RCRs > 1 were the same by Stoffenmanager® and ART, but not by ECETOC TRA. Stoffenmanager and ART identified 25 adjusted scenarios with RCR > 1, while ECETOC TRA gave RCR < 1 for the same scenarios. The ES with RCR > 1 were significantly associated to chemicals with higher vapour pressure and lower DNELs than ES with RCR < 1 by Stoffenmanager®. The correlations between observed and registered RCRs were lower than those between RCRs calculated from the different models themselves; ECETOC TRA had the lowest correlation with the registered ES. These results put in question the generic ES recommended under the REACH legislation. Downstream users may get better estimates by assessing their own ES, especially for chemicals with low DNELs and high vapour pressure.

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.

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.

Health risk assessment from exposure to particles during packing in working environments

Packing of raw materials in work environments is a known source of potential health impacts (respiratory, cardiovascular) due to exposure to airborne particles. This activity was selected to test different exposure and risk assessment tools, aiming to understand the effectiveness of source enclosure as a strategy to mitigate particle release. Worker exposure to particle mass and number concentrations was monitored during packing of 7 ceramic materials in 3 packing lines in different settings, with low (L), medium (M) and high (H) degrees of source enclosure. Results showed that packing lines L and M significantly increased exposure concentrations (119-609 μg m^-3 respirable, 1150-4705 μg m^-3 inhalable, 24,755-51,645 cm^-3 particle number), while non-significant increases were detected in line H. These results evidence the effectiveness of source enclosure as a mitigation strategy, in the case of packing of ceramic materials. Total deposited particle surface area during packing ranged between 5.4 and 11.8 × 10⁵ μm² min^-1, with particles depositing mainly in the alveoli (51-64%) followed by head airways (27-41%) and trachea bronchi (7-10%). The comparison between the results from different risk assessment tools (Stoffenmanager, ART, NanoSafer) and the actual measured exposure concentrations evidenced that all of the tools overestimated exposure concentrations, by factors of 1.5-8. Further research is necessary to bridge the current gap between measured and modelled health risk assessments.

Source specific exposure and risk assessment for indoor aerosols

Poor air quality is a leading contributor to the global disease burden and total number of deaths worldwide. Humans spend most of their time in built environments where the majority of the inhalation exposure occurs. Indoor Air Quality (IAQ) is challenged by outdoor air pollution entering indoors through ventilation and infiltration and by indoor emission sources. The aim of this study was to understand the current knowledge level and gaps regarding effective approaches to improve IAQ. Emission regulations currently focus on outdoor emissions, whereas quantitative understanding of emissions from indoor sources is generally lacking. Therefore, specific indoor sources need to be identified, characterized, and quantified according to their environmental and human health impact. The emission sources should be stored in terms of relevant metrics and statistics in an easily accessible format that is applicable for source specific exposure assessment by using mathematical mass balance modelings. This forms a foundation for comprehensive risk assessment and efficient interventions. For such a general exposure assessment model we need 1) systematic methods for indoor aerosol emission source assessment, 2) source emission documentation in terms of relevant a) aerosol metrics and b) biological metrics, 3) default model parameterization for predictive exposure modeling, 4) other needs related to aerosol characterization techniques and modeling methods. Such a general exposure assessment model can be applicable for private, public, and occupational indoor exposure assessment, making it a valuable tool for public health professionals, product safety designers, industrial hygienists, building scientists, and environmental consultants working in the field of IAQ and health.

Testing the performance of one and two box models as tools for risk assessment of particle exposure during packing of inorganic fertilizer

Modelling of particle exposure is a useful tool for preliminary exposure assessment in workplaces with low and high exposure concentrations. However, actual exposure measurements are needed to assess models reliability. Worker exposure was monitored during packing of an inorganic granulate fertilizer at industrial scale using small and big bags. Particle concentrations were modelled with one and two box models, where the emission source was estimated with the fertilizer's dustiness index. The exposure levels were used to calculate inhaled dose rates and test accuracy of the exposure modellings. The particle number concentrations were measured from worker area by using a mobility and optical particle sizer which were used to calculate surface area and mass concentrations. The concentrations in the worker area during pre-activity ranged 63,797-81,073 cm^-3, 4.6 × 10⁶ to 7.5 × 10⁶ μm² cm^-3, and 354 to 634 μg m^-3 (respirable mass fraction) and during packing 50,300 to 85,949 cm^-3, 4.3 × 10⁶ to 7.6 × 10⁶ μm² cm^-3, and 279 to 668 μg m^-3 (respirable mass fraction). Thus, the packing process did not significantly increase the exposure levels. Chemical exposure was also under control based on REACH standards. The particle surface area deposition rate in respiratory tract was up to 7.6 × 10⁶ μm² min^-1 during packing, with 52%-61% of deposition occurring in the alveolar region. Ratios of the modelled and measured concentrations were 0.98 ± 0.19 and 0.84 ± 0.12 for small and big bags, respectively, when using the one box model, and 0.88 ± 0.25 and 0.82 ± 0.12, when using the two box model. The modelling precision improved for both models when outdoor particle concentrations were included. This study shows that exposure concentrations in a low emission industrial scenario, e.g. during packing of a fertilizer, can be predicted with a reasonable accuracy by using the concept of dustiness and mass balance models.

REACH Worker Exposure Model for Co-formulants Used in Plant Protection Products

Background

Substances used as co-formulants in plant protection products (PPP) may require registration under Regulation (EC) No. 1907/2006 (REACH), and additionally where an exposure assessment is required, this must take into consideration the specifics of the PPP use.

Objectives

This work reports a customized screening level model developed to support human health risk assessment of operators, workers, and bystanders (OWB) for co-formulants used in PPP. The OWB model was designed to closely integrate with REACH generic exposure scenarios (GES) for PPP developed by the European Crop Protection Association (ECPA). The use of these tools in combination is expected to lead to a more standardized and hence efficient risk assessment of co-formulants. This study describes the basis for OWB exposure predictions as well as benchmarking against relevant REACH exposure models for equivalent tasks. The benchmarking was carried out to gain some insight into the initial assumption that the most commonly used tier 1 REACH model would be more conservative than the specific PPP models used for regulatory risk assessments under PPP legislation.

Method

Existing exposure models with regulatory acceptance for the most common types of PPP and their professional and consumer uses were selected. The German BBA model was used to assess spray applications. Granule and seed dispersal was assessed using the US Environmental Protection Agency (EPA) Pesticide Handlers Exposure Database (PHED). ECETOC TRA was employed to assess exposure during certain tasks performed in seed treatment, not covered by these PPP models. Where the underlying models featured multiple exposure determinants, the exposure was calculated for all permutations, and the worst-case exposure selected and reported for use in risk assessment. The PPP models are based on measured data collected during actual application of PPP; hence, the worst-case exposure predicted was expected to reflect a realistic worst case for these tasks.

Results

OWB was implemented as an Excel spreadsheet. Exposure models, parameters, and exposure and risk estimates are reported in a REACH-compliant output format to facilitate the registration of co-formulant uses. As would be expected, benchmarking OWB against the PPP-specific exposure models demonstrated equivalence with the worst-case prediction from these underlying PPP models. For the scenarios modelled, the tier 1 ECETOC TRA gave more conservative predictions than OWB. The reduction in conservatism is attributed to the underlying PPP models being based on measured data collected specifically during the use of PPP, compared to the data underlying ECETOC TRA, based mainly on industrial workplace uses.

Conclusions

OWB provides inhalation and dermal exposure estimates for co-formulants used in PPP which are equivalent to the worst-case estimates from existing specialized PPP exposure models based on measured data. OWB has simplified information requirements in comparison to higher-tier REACH or PPP models. Use of OWB in combination with the defined ECPA GES facilitates an efficient and standardized REACH risk assessment and registration of co-formulant uses in PPP. A defined assessment framework and default inputs potentially decreases the anticipated inter-user variability compared with the use of higher-tier PPP or REACH models in this screening level context.

Qualitative and quantitative differences between common control banding tools for nanomaterials in workplaces

A number of control banding (CB) tools have been developed specifically for managing the risk of exposure to engineered nanomaterials. However, data on the methodological differences between common CB tools for nanomaterials in workplaces are rare. A comparative study with different CB tools, such as Nanosafer, Stoffenmanager-Nano, Nanotool, Precautionary Matrix, ECguidance, IVAM Guidance, ISO, and ANSES, was performed to investigate their qualitative and quantitative differences in real exposure scenarios. These tools were developed for different purposes, with different application domains, methodological principles, and criteria. Multi-criteria analysis showed that there was a diverse distribution of these eight CB tools across different evaluation indicators. The total evaluation scores for Nanotool, Stoffenmanager-Nano, and Nanosafer were higher than the other tools. Quantitative comparisons demonstrated that ANSES, ECguidance, and IVAM Guidance tools were better in terms of information availability. Nanotool, Stoffenmanager-Nano, and ECguidance were better in terms of the sensitivity of outputs to changes in exposure parameters. The Nanotool, ANSES, and ECguidance tools were better in terms of accuracy of hazard outcomes evaluated with toxicological data. The Stoffenmanager-Nano, Nanotool, and Nanosafer tools' exposure scores for seven scenarios had a good correlation with measurement data. The Nanotool and Stoffenmanager-Nano tools had much higher comprehensive advantages based on quantitative and qualitative assessment. More comparative studies evaluating different tools are required, using more types of nanomaterials in real exposure scenarios.

A number of control banding (CB) tools have been developed specifically for managing the risk of exposure to engineered nanomaterials.

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.

The general ventilation multipliers calculated by using a standard Near-Field/Far-Field model

In conceptual exposure models, the transmission of pollutants in an imperfectly mixed room is usually described with general ventilation multipliers. This is the approach used in the Advanced REACH Tool (ART) and Stoffenmanager® exposure assessment tools. The multipliers used in these tools were reported by Cherrie (1999; http://dx.doi.org/10.1080/104732299302530 ) and Cherrie et al. (2011; http://dx.doi.org/10.1093/annhyg/mer092 ) who developed them by positing input values for a standard Near-Field/Far-Field (NF/FF) model and then calculating concentration ratios between NF and FF concentrations. This study revisited the calculations that produce the multipliers used in ART and Stoffenmanager and found that the recalculated general ventilation multipliers were up to 2.8 times (280%) higher than the values reported by Cherrie (1999) and the recalculated NF and FF multipliers for 1-hr exposure were up to 1.2 times (17%) smaller and for 8-hr exposure up to 1.7 times (41%) smaller than the values reported by Cherrie et al. (2011). Considering that Stoffenmanager and the ART are classified as higher-tier regulatory exposure assessment tools, the errors is general ventilation multipliers should not be ignored. We recommend revising the general ventilation multipliers. A better solution is to integrate the NF/FF model to Stoffenmanager and the ART.