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Several exposure assessment models use dustiness as an input parameter for scaling or estimating exposure during powder handling. Use of different dustiness methods will result in considerable differences in the dustiness values as they are based on different emission generation principles. EN17199:2019 offers 4 different dustiness test methods considering different dust release scenarios (e.g. powder pouring, mixing and gentle agitation, and vibration). Conceptually, the dustiness value by a given method can be multiplied with a scenario-specific modifier, called a handling energy factor (H i ), that allows conversion of a dustiness value to a release constant. Therefore, a H i , scaling the effective mechanical energy in the process to the energy supplied in the specific dustiness test, needs to be applied. To improve the accuracy in predictive exposure modelling, we derived experimental H i to be used in exposure algorithms considering both the mass- and number-based dust release fraction determined by the EN17199-3 continuous drop (CD) and the EN17199-4 small rotating drum (SRD) test methods. Three materials were used to evaluate the relationship between dustiness and dust levels during pouring powder from different heights in a controlled environment. The results showed increasing scatter and difference between the H i derived for the 2 test methods with increasing pouring height. Nearly all the H i values obtained for both SRD and CD were <1 indicating that the dustiness tests involved more energy input than the simulated pouring activity and consequently de-agglomeration and dust generation were higher. This effect was most pronounced in CD method showing that SRD mechanistically resembles more closely the powder pouring. [ABSTRACT FROM AUTHOR]

Task-based respirable crystalline silica (RCS) exposure monitoring data was collected from construction work sites across 3 Canadian provinces: Alberta, British Columbia (BC), and Manitoba. In total 373 RCS samples were obtained from 70 worksites across 44 companies. Sampling was conducted between May 2015 and August 2020. The overall geometric mean (GM) RCS exposure was 0.045 mg/m3 (geometric standard deviation, GSD = 6.8). Alberta had the highest average exposure and the highest variability with GM of 0.060 mg/m3 (GSD = 9.3), the GM in BC was 0.044 (GSD = 4.3), and in Manitoba the GM was 0.033 (GSD = 7.0). A multivariable model was built using forward stepwise linear regression modeling. Province, task type, work environment (indoor vs. outdoor), construction material, sampling duration, and engineering control use were all statistically significant predictors of exposure level in partial F -tests (P < 0.05). Overall, the model explained 42% of the RCS concentration variability. Task type contributed most to the model's explanatory power. The task type with highest average exposure levels was demolition (GM 0.30 mg/m3, GSD 0.49). Breaking (GM 0.16 mg/m3, GSD 8.4) and grinding (GM 0.081 m/m3, GSD 7.4) also had high-exposure levels. Working outdoors was associated with exposure levels 39% lower than indoors. Exposure control measures such as local exhaust ventilation and wetting were also associated with lower exposure levels. Among construction materials, Cement, sand, and stone were associated with higher RCS exposure levels relative to the reference material, concrete. The results of this study indicate that workers in western Canada remain exposed to RCS at levels that exceed the health-based American Congress for Governmental Industrial Hygienists Threshold Limit Value of 0.025 mg/m3. Although there were some differences in exposure levels between the provinces, the determinants of exposure were similar in all 3. The overall GM RCS exposure was 0.045 mg/m3 (geometric standard deviation, GSD = 6.8). Alberta had the highest average exposure and the highest variability with GM of 0.060 mg/m3 (GSD = 9.3), the GM in BC was 0.044 (GSD = 4.3), and in Manitoba the GM was 0.033 (GSD = 7.0). [ABSTRACT FROM AUTHOR]

Background Occupational exposure to soluble chlorinated platinum (Pt) salts, commonly called chloroplatinates, is a known cause of Pt salt sensitisation (PSS) and occupational asthma. We aimed to model inhalable soluble Pt salts exposure levels based on measurements in precious metal refineries for use in a retrospective cohort study on PSS. Methods Five platinum refineries located in the United Kingdom (3 sites), United States, and South Africa provided time weighted average inhalable soluble Pt salts exposure data, measured in 2,982 personal air samples over a 17-year period (2000-2016). We used a Bayesian hierarchical model to estimate geometric mean (GM) exposure levels for each refinery and job title over time. Results The GM of measured exposure levels over all facilities was 92 ng/m3 with a geometric standard deviation (GSD) of 9.07. Facility-specific GMs ranged from 48 ng/m3 (GSD 15.3) to 242 ng/m3 (GSD 5.99). Exposure modelling showed that soluble Pt salts exposure levels declined approximately 10% per year in two of the five facilities, but there were no clear time trends in the other facilities. A priori specified exposure groups captured most of the between-jobs differences, which helps to accurately predict exposures for jobs with no measurement data available. Conclusions We applied exposure modelling to estimate time, refinery, and job-specific soluble Pt salts exposures. A significant annual decline in exposure levels was observed in two of the five participating facilities. Modelled exposure levels can be linked to individual workers' job history for exposure–response analysis of PSS in an epidemiological study. [ABSTRACT FROM AUTHOR]

Wood dust is an established carcinogen also linked to several non malignant respiratory disorders. A major limitation in research on wood dust and its health effects is the lack of (historical) quantitative estimates of occupational exposure for use in general population-based case-control or cohort studies. The present study aimed to develop a multinational quantitative Job Exposure Matrix (JEM) for wood dust exposure using exposure data from several Northern and Central European countries. For this, an occupational exposure database containing 12653 personal wood dust measurements collected between 1978 and 2007 in Denmark, Finland, France, The Netherlands, Norway, and the United Kingdom (UK) was established. Measurement data were adjusted for differences in inhalable dust sampling efficiency resulting from the use of different dust samplers and analysed using linear mixed effect regression with job codes (ISCO-88) and country treated as random effects. Fixed effects were the year of measurement, the expert assessment of exposure intensity (no, low, and high exposure) for every ISCO-88 job code from an existing wood dust JEM and sampling duration. The results of the models suggest that wood dust exposure has declined annually by approximately 8%. Substantial differences in exposure levels between countries were observed with the highest levels in the United Kingdom and the lowest in Denmark and Norway, albeit with similar job rankings across countries. The jobs with the highest predicted exposure are floor layers and tile setters, wood-products machine operators, and building construction labourers with geometric mean levels for the year 1997 between 1.7 and 1.9 mg/m3. The predicted exposure estimates by the model are compared with the results of wood dust measurement data reported in the literature. The model predicted estimates for full-shift exposures were used to develop a time-dependent quantitative JEM for exposure to wood dust that can be used to estimate exposure for participants of general population studies in Northern European countries on the health effects from occupational exposure to wood dust. [ABSTRACT FROM AUTHOR]

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

In this article, we have responded to the key statements in the article by Koivisto et al. (2022) that were incorrect and considered to be a biased critique on a subset of the exposure models used in Europe (i.e. ART and Stoffenmanager®) used for regulatory exposure assessment. We welcome scientific discussions on exposure modelling (as was done during the ISES Europe workshop) and criticism based on scientific evidence to contribute to the advancement of occupational exposure estimation tools. The tiered approach to risk assessment allows various exposure assessment models from screening tools (control/hazard banding) through to higher-tiered approaches. There is a place for every type of model, but we do need to recognize the cost and data requirements of highly bespoke assessments. That is why model developers have taken pragmatic approaches to develop tools for exposure assessments based on imperfect data. We encourage Koivisto et al. to focus on further scientifically robust work to develop mass-balance models and by independent external validations studies, compare these models with alternative model tools such as ART and Stoffenmanager®. [ABSTRACT FROM AUTHOR]

Objectives To use the recently developed Ontario Mining Exposure Database (OMED) to describe historical silica exposure in the Ontario metal mining industry and identify predictors of historical silica exposure. Methods Personal respirable crystalline silica (RCS) data for metal mines were extracted from OMED and included both individual and summary measures, where multiple exposure measurements (n > 1) were aggregated and entered as a single exposure value (n = 1). Data were stratified by sample location (underground/surface) for analysis. Monte Carlo simulation was used to simulate individual measures from the summary measures. A fixed effects multiple linear regression model was used to assess the effects of commodity (ore mined), sample year, source of exposure data, and occupational group on RCS concentration. Parameter estimates (β), standard errors, and 95% upper and lower confidence intervals were reported. Results The OMED contained 12 995 silica measurements. After limiting to RCS measurements in metal mines, and measures with sufficient information for analysis, 2883 RCS measurements collected from 1974 to 1991 remained, including 2816 individual and 67 summary measurements. In total, 321 individual RCS measurements were simulated from the 67 summary measures. The analysis database contained 2771 (12% simulated) underground measurements and 366 surface measurements (0% simulated). In the underground group, an overall geometric mean (GM) of 0.05 [geometric standard deviation (GSD) 3.09] mg m−3 was estimated with a 6% annual decrease over time. In this group, the commodity with the highest average RCS level was zinc mines (GM = 0.07 mg m−3) and the lowest was iron mines (GM = 0.01 mg m−3). In the surface group, an overall GM of 0.05 (GSD 3.70) mg m−3 was estimated with an 8% decreased over time. In this group, the commodity with the highest average RCS level was gold mines (GM = 0.07 mg m−3) and the lowest was zinc mines (GM = 0.03 mg m−3). In both groups, company collected data had lower estimated RCS compared with regulator collected data. Conclusions Historical RCS levels decreased over time. Mean measurements exceeded the American Conference of Governmental Industrial Hygienists current health-based threshold limit value (0.025 mg m−3). The main predictors of exposure were commodity, source of exposure data, and sample year. However, low R 2 and high GSD values suggest additional predictors of RCS exposures in Ontario's metal mines exist that were unavailable in OMED. [ABSTRACT FROM AUTHOR]

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

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

This study aims to evaluate the accuracy, precision and conservatism of three models (ECETOC TRA 3.1, Stoffenmanager 7.0 and ART 1.5) by comparing model predictions and repeated exposure measurements in Korea. We collected the exposure measurements (n = 390) and detailed contextual information extracted from 10 survey reports published by Ministry of Employment and Labour in the mid-2000s. Using these three models, seven occupational health professionals predicted inhalation exposure to 10 solvents used for cleaning tasks in 51 situations at 33 companies in 15 industries. We applied four comparison approaches previously used by several European studies and calculated the lack of agreement (bias, relative bias, precision), Pearson correlation coefficients (r), level of conservatism, and residuals between the log-transformed predicted estimates and measured exposures on both individual and situation levels. The overall bias and precision were −0.53 ± 2.11 with ART, −1.32 ± 5.88 with Stoffenmanager, and −1.03 ± 8.88 with ECETOC TRA. Pearson correlation coefficients were significantly high in ART (r = 0.95) and Stoffenmanager (r = 0.82), but moderate in ECETOC TRA (r = 0.58). We found ART to be the most accurate model, and Stoffenmanager was the most balanced model in terms of good accuracy, high correlation, and medium conservatism in the model predictions. However, ECETOC TRA showed less accurate outcomes and lower level of conservatism but still had moderate correlations. We observed a systematic tendency to overestimate low exposures and underestimate higher exposures in all models, similar to previous studies. Therefore, our findings suggest that these European models can be used to predict occupational exposure to solvents in Korea. [ABSTRACT FROM AUTHOR]

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

Background: Nano-specific inhalation exposure models could potentially be effective tools to assess and control worker exposure to nano-objects, and their aggregates and agglomerates (NOAA). However, due to the lack of reliable and consistent collected NOAA exposure data, the scientific basis for validation of the existing NOAA exposure models is missing or limited. The main objective of this study was to gain more insight into the effect of various determinants underlying the potential on the concentration of airborne NOAA close to the source with the purpose of providing a scientific basis for existing and future exposure inhalation models. Method: Four experimental studies were conducted to investigate the effect of 11 determinants of emission on the concentration airborne NOAA close to the source during dumping of ~100% nanopowders. Determinants under study were: nanomaterial, particle size, dump mass, height, rate, ventilation rate, mixing speed, containment, particle surface coating, moisture content of the powder, and receiving surface. The experiments were conducted in an experimental room (19.5 m³) with well-controlled environmental and ventilation conditions. Particle number concentration and size distribution were measured using real-time measurement devices. Results: Dumping of nanopowders resulted in a higher number concentration and larger particles than dumping their reference microsized powder (P < 0.05). Statistically significant more and larger particles were also found during dumping of SiO2 nanopowder compared to TiO2/Al2O3 nanopowders. Particle surface coating did not affect the number concentration but on average larger particles were found during dumping of coated nanopowders. An increase of the powder's moisture content resulted in less and smaller particles in the air. Furthermore, the results indicate that particle number concentration increases with increasing dump height, rate, and mass and decreases when ventilation is turned on. Discussion: These results give an indication of the direction and magnitude of the effect of the studied determinants on concentrations close to the source and provide a scientific basis for (further) development of existing and future NOAA inhalation exposure models. [ABSTRACT FROM AUTHOR]

Background: Control banding can be used as a first-tier assessment to control worker exposure to nano-objects and their aggregates and agglomerates (NOAA). In a second tier, more advanced modelling approaches are needed to produce quantitative exposure estimates. As currently no general quantitative nano-specific exposure models are available, this study evaluated the validity and applicability of using a generic exposure assessment model (the Advanced REACH Tool--ART) for occupational exposure to NOAA. Method: The predictive capability of ART for occupational exposure to NOAA was tested by calculating the relative bias and correlations (Pearson) between the model estimates and measured concentrations using a dataset of 102 NOAA exposure measurements collected during experimental and workplace exposure studies. Results: Moderate to (very) strong correlations between the ART estimates and measured concentrations were found. Estimates correlated better to measured concentration levels of dust (r = 0.76, P < 0.01) than liquid aerosols (r = 0.51, P = 0.19). However, ART overestimated the measured NOAA concentrations for both the experimental and field measurements (factor 2--127). Overestimation was highest at low concentrations and decreased with increasing concentration. Correlations seemed to be better when looking at the nanomaterials individually compared to combined scenarios, indicating that nanomaterial-specific characteristics are not well captured within the mechanistic model of the ART. Discussion: Although ART in its current state is not capable to estimate occupational exposure to NOAA, the strong correlations for the individual nanomaterials indicate that the ART (and potentially other generic exposure models) have the potential to be extended or adapted for exposure to NOAA. In the future, studies investigating the potential to estimate exposure to NOAA should incorporate more explicitly nanomaterial-specific characteristics in their models. [ABSTRACT FROM AUTHOR]

Stoffenmanager is an exposure and risk assessment tool that has a control banding part, with risk bands as outcome and a quantitative exposure assessment part, with the 90th percentile of the predicted exposure as a default outcome. The main aim of the study was to investigate whether multiple users of Stoffenmanager came to the same result when modelling the same scenarios. Other aims were to investigate the differences between outcomes of the control banding part with the measured risk quota and to evaluate the conservatism of the tool by testing whether the 90th percentiles are above the measured median exposures. We investigated airborne exposures at companies in four different types of industry: wood, printing, metal foundry, and spray painting. Three scenarios were modelled and measured, when possible, at each company. When modelled, 13 users visited each company on the same occasion creating individual assessments. Consensus assessments were also modelled for each scenario by six occupational hygienists. The multiple users' outcomes were often spread over two riskbands in the control banding part, and the differences in the quantitative exposure outcomes for the highest and lowest assessments per scenario varied between a factor 2 and 100. Four parameters were difficult for the users to assess and had a large impact on the outcome: type of task, breathing zone, personal protection, and control measures. Only two scenarios had a higher measured risk quota than predicted by the control banding part, also two scenarios had slightly higher measured median exposure value than modelled consensus in the quantitative exposure assessment part. Hence, the variability between users was large but the model performed well. [ABSTRACT FROM AUTHOR]

Regression models are being used to quantify the effect of an exposure on an outcome, while adjusting for potential confounders. While the type of regression model to be used is determined by the nature of the outcome variable, e.g. linear regression has to be applied for continuous outcome variables, all regression models can handle any kind of exposure variables. However, some fundamentals of representation of the exposure in a regression model and also some potential pitfalls have to be kept in mind in order to obtain meaningful interpretation of results. The objective of this educational paper was to illustrate these fundamentals and pitfalls, using various multiple regression models applied to data from a hypothetical cohort of 3000 patients with chronic kidney disease. In particular, we illustrate how to represent different types of exposure variables (binary, categorical with two or more categories and continuous), and how to interpret the regression coefficients in linear, logistic and Cox models. We also discuss the linearity assumption in these models, and show how wrongly assuming linearity may produce biased results and how flexible modelling using spline functions may provide better estimates. [ABSTRACT FROM AUTHOR]

Objectives: The aims of this study were to estimate inhalation exposure to chemicals and the resulting acute health risks for working scenarios characterized by successive peaks of pollutant concentrations. Methods: A stochastic two-zone model combining a time-varying emission function and field-derived probabilistic distributed input parameter was used to predict both instantaneous and 15-min averaged pollutant concentrations during the decanting operations performed in a pathology laboratory. The location of the workers was taken into account in the model for computing probability distributions of inhalation exposures and for subsequently characterizing hazard quotients (HQ) for health risk purposes. The model was assessed by comparison with repeated individual monitoring performed on the workers during the same tasks. Results: Modelled inhalation exposure profiles revealed 15-min average concentrations of 1.7 and 208 mg m−3 for formaldehyde (FA) and toluene (TOL), respectively. The individual monitoring performed showed similar average concentrations, with 1.2 and 175 mg m−3 for FA and TOL. No more than three to five successive FA concentration peaks were generally sufficient in the modelling exercise to provide 15-min estimated exposures exceeding short-term exposure limits (STEL). Modelled HQ higher than unity and STEL exceedance probabilities higher than 0.5 were found for FA, whereas estimated TOL health risks were notably lower according to high exposure limits. Estimated inhalation exposure distributions frequently ranged over one order of magnitude for the two pollutants, reflecting both the natural exposure variability and the uncertainty of some of the two-zone model input parameters. Conclusions: These findings indicate that the developed approach may be useful for modelling occupational exposures and acute health risks related to chemicals in situations involving time-varying emission sources. Modelled exposure distributions may also be used within Bayesian decision analysis frameworks for making exposure judgements and refining risk management measures. [ABSTRACT FROM AUTHOR]

Airborne grain dust is a complex mixture of fragments of organic material from grain, plus mineral matter from soil, and possible insect, fungal, or bacterial contamination or their toxic products, such as endotoxin. In the 1990s, grain workers in Britain were frequently exposed to inhalable dust >10 mg.m−3 (8 h), with particularly high exposures being found at terminals where grain was imported or exported and in drying operations (personal exposure typically ∼20 mg.m−3). Since then, the industry has made substantial progress in improving the control of airborne dust through better-designed processes, increased automation, and an improved focus on product quality. We have used information from the published scientific literature and a small survey of industry representatives to estimate current exposure levels. These data suggest that current long-term exposure to inhalable dust for most workers is on average less than ∼3 mg.m−3, with perhaps 15–20% of individual personal exposures being >10 mg.m−3. There are no published data from Britain on short-term exposure during cleaning and other tasks. We have estimated average levels for a range of tasks and judge that the highest levels, for example during some cleaning activities and certain process tasks such as loading and packing, are probably ∼10 mg.m−3. Endotoxin levels were judged likely to be <104 EU m−3 throughout the industry provided inhalable dust levels are <10 mg.m−3. There are no published exposure data on mycotoxin, respirable crystalline silica, and mite contamination but these are not considered to present widespread problems in the British industry. Further research should be carried out to confirm these findings. [ABSTRACT FROM AUTHOR]

The Advanced REACH Tool (ART) is an exposure assessment tool that combines mechanistically modelled inhalation exposure estimates with available exposure data using a Bayesian approach. The mechanistic model is based on nine independent principal modifying factors (MF). One of these MF is the substance emission potential, which addresses the intrinsic substance properties as determinants of the emission from a source. This paper describes the current knowledge and evidence on intrinsic characteristics of solids and liquids that determine the potential for their release into workplace air. The principal factor determining the release of aerosols from handling or processing powdered, granular, or pelletized materials is the dustiness of the material, as well as the weight fraction of the substance of interest in the powder and the moisture content. The partial vapour pressure is the main intrinsic factor determining the substance emission potential for emission of vapours. For generation of mist, the substance emission potential is determined by the viscosity of the liquid as well as the weight fraction of the substance of interest in the liquid. Within ART release of vapours is considered for substances with a partial vapour pressure at the process temperature of 10 Pa or more, while mist formation is considered for substances with a vapour pressure ≤10 Pa. Relative multipliers are assigned for most of the intrinsic factors, with the exception of the weight fraction and the vapour pressure, which is applied as a continuous variable in the estimation of the substance emission potential. Currently, estimation of substance emission potential is not available for fumes, fibres, and gases. The substance emission potential takes account of the latest thinking on emissions of dusts, mists, and vapours and in our view provides a good balance between theory and pragmatism. Expanding the knowledge base on substance emission potential will improve the predictive power of occupational exposure models and thereby the accuracy and precision of the exposure estimates. [ABSTRACT FROM AUTHOR]

This paper provides an outline of the Advanced REACH Tool (ART) version 1.0 and a discussion of how it could be further developed. ART is a higher tier exposure assessment tool that combines mechanistically modelled inhalation exposure predictions with available exposure data using a Bayesian approach. ART assesses exposure for scenarios across different plants and sites. Estimates are provided for different percentiles of the exposure distribution and confidence intervals around the estimate. It also produces exposure estimates in the absence of data, but uncertainty of the estimates will decrease when results of exposure measurements are included. The tool has been calibrated using a broad range of exposure data and provides estimates for exposure to vapours, mists, and dusts. ART has a robust and stable conceptual basis but will be refined in the future and should therefore be considered an evolving system. High-priority areas for future research are identified in this paper and include the integration of partially analogous measurement series, inclusion of company and site-specific assessments, user decision strategies linked to ART predictions, evaluation of validity and reliability of ART, exploring the possibilities for incorporating the dermal route and integration of ART predictions with tools for modelling internal dose. ART is initially developed in the scope of REACH but is equally useful for exposure assessment in other areas. [ABSTRACT FROM PUBLISHER]

Objectives: Confronted by variable exposure scenarios characterized by intermittent concentration peaks, our study aimed to develop methods and determine mathematical functions reproducing organic solvent concentration profiles in order to assess health risks.Methods: Two similar repetitive decanting tasks using either formalin or toluene (TOL) were studied at a teaching hospital pathology laboratory. Real-time air monitoring performed in the immediate vicinity of pollutant sources over a 1-year period identified intermittent concentration peaks. In order to describe these specific exposure profiles, two different methods were used. In a first descriptive approach, concentration peaks were either assimilated to an equivalent series of rectangle functions or described by a mathematical bell-shaped function. As an alternative approach, a model based on the schedule of decanting tasks was constructed. To this end, a time-varying emission function was incorporated into three deterministic exposure models of increasing complexity (well-mixed room, two-zone, spherical turbulent diffusion) and field-derived emission parameters were estimated by fitting model outputs to measured concentration profiles.Results: Real-time measurements revealed highly variable concentration profiles, consisting of 1–8 peaks ranging from 5 to 220 s per decanting task, and average concentrations within peaks varying over 1–2 orders of magnitude. Acceptable fits were obtained by both descriptive approaches. The tested emission function seemed relevant in reproducing intermittent pollutant releases. Only advanced models (two-zone and diffusion) gave satisfying fits within assigned input parameter ranges. Average emission rate estimates varied in the range 10–47 mg min−1 for formaldehyde and 360–1780 mg min−1 for TOL, depending on the model tested.Conclusions: Both descriptive approaches and deterministic models accurately reproduced the patterns of measured concentration peaks. However, only deterministic models provided an understanding of the relations between pollutant releases, air movements, and the resulting concentrations and may thus be recommended for exposure variability assessment purposes. [ABSTRACT FROM PUBLISHER]

The regulatory risk assessment of chemicals requires the estimation of occupational dermal exposure. Until recently, the models used were either based on limited data or were specific to a particular class of chemical or application. The EU project RISKOFDERM has gathered a considerable number of new measurements of dermal exposure together with detailed contextual information. This article describes the development of a set of generic task-based models capable of predicting potential dermal exposure to both solids and liquids in a wide range of situations. To facilitate modelling of the wide variety of dermal exposure situations six separate models were made for groupings of exposure scenarios called Dermal Exposure Operation units (DEO units). These task-based groupings cluster exposure scenarios with regard to the expected routes of dermal exposure and the expected influence of exposure determinants. Within these groupings linear mixed effect models were used to estimate the influence of various exposure determinants and to estimate components of variance. The models predict median potential dermal exposure rates for the hands and the rest of the body from the values of relevant exposure determinants. These rates are expressed as mg or μl product per minute. Using these median potential dermal exposure rates and an accompanying geometric standard deviation allows a range of exposure percentiles to be calculated. [ABSTRACT FROM AUTHOR]

Occupational exposure to chemical agents can vary enormously within- and between-workers, even when carrying out the same jobs. When repeated measurements are available, the variance components can be estimated using random- or mixed-effects models. Pooling the variance components across the fixed effects, in mixed-effects models, reduces the complexity of the models; especially, when there are a large number of fixed effects. The analyses presented in this paper tested the assumptions of homogeneity in the variance components between factories and surveys for inhalable dust exposure in the European carbon black manufacturing industry. In total, 5296 measurements from 1771 workers were available collected during two surveys carried out between 1991 and 1995. Workers were grouped into eight job categories, and for each of these separate mixed-effects models were developed, including factory, survey and in some cases the interaction term as the fixed effects. The likelihood ratio test was used to test the assumptions of homogeneity of the variance components. Statistically significant heterogeneity of the variance components was observed for two of the eight job categories, ‘Fitter/Welder’ and ‘Warehouseman’. The heterogeneity was due mainly to differences in variance between the factories. When estimating the probability of overexposure for all the factories combined, there was little difference between the models with and without heterogeneous variance components for ‘Fitters/Welders’. For the ‘Warehousemen’ the probability of overexposure in the last survey changed marginally from 4% in the pooled model to 6% in the heterogeneous model. Larger differences between the models were observed when estimating the probability of overexposure for individual factories, which was due to over- or under-estimation of the variance components in the pooled models. In conclusion, for most job categories pooling of the variance components appears to be justified in this database. In addition, no large differences were found when determining the industry-wide probability of ‘overexposure’ when comparing the pooled with the heterogeneous models. However, when evaluating the factory-specific probability of ‘overexposure’ or when using the models to provide exposure estimates for epidemiological studies heterogeneity in the variance components should be investigated. [ABSTRACT FROM AUTHOR]

The estimation and assessment of substance exposure (EASE) model has been under development and in use since the early 1990s. It is a general model that can be used to predict workplace exposure to any substance hazardous to health. The current EASE model (version 2.0) has been used widely in the risk assessment of new and existing chemicals by the UK Health and Safety Executive (HSE) and other regulatory agencies. EASE has also been distributed globally to over 200 users and therefore may have been used for many other purposes. Despite widespread use of the model, neither the development of its structure nor its underlying concepts and principles have been published in the open literature. Using surviving documentary evidence and discussions with key personnel, the creation and development of the model from 1992 to 2002 is described. The role of the HSE's National Exposure Database (NEDB) as the principal data source for the development of the model output exposure ranges is described. A number of problems and limitations of the model have been identified and the description of the model's development provides some explanation of their presence. [ABSTRACT FROM AUTHOR]

This paper presents an in-house BAuA study on the evaluation of the COSHH Essentials exposure predictive model. External validation is based on measurement data obtained in BAuA field studies performed in various industries, e.g. printing industry and textile industry. In addition, measurement data and information on industrial hygiene provided by the chemical industry within the framework of the Existing Substances Risk Assessment programme are used. Although the evaluated exposure data cover a wide variety of activities and workplace scenarios, there is still a considerable lack of appropriate exposure data, especially for the more stringent control strategies. It was found that the level of agreement between the measurements for solid substances (powders, dusts) and the predicted ranges is reasonably good. The situation is in part different for liquids. In workplaces where organic solvents are used in litre quantities, exposure levels are within the predicted ranges or are often lower. For small-scale uses of liquids (millilitre scale), e.g. in carpenters’ workshops, there were indications that the exposure levels can exceed the predicted ranges. However, it must be noted that the database is rather small. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) is employed to simulate breathing-zone concentration for a simple representation of spray painting a flat plate in a cross-flow ventilated booth. The results demonstrate the capability of CFD to track correctly changes in breathing-zone concentration associated with work practices shown previously to be significant in determining exposure. Empirical data, and models verified through field studies, are used to examine the predictive capability of these simulations and to identify important issues in the conduct of such comparisons. A commercially available CFD package is used to solve a three-dimensional turbulent flow problem for the velocity field, and to subsequently generate particle trajectories for polydisperse aerosols. An in-house algorithm is developed to convert the trajectory data to breathing-zone concentrations, transfer efficiencies and aerosol size distributions. The mesh size, time step, duration of the simulation, and number of particles per size interval are all important variables in achieving convergent results. [ABSTRACT FROM PUBLISHER]

Proper use information is necessary to do exposure modelling in the scope of regulatory risk assessments. The legal regime under which the regulatory risk assessment is done influences the need for use information. Different legal regimes lead to more or less possibilities for authorities to define the exposure scenario to be assessed and the data to be presented by the industrial parties involved. The more information is required by the regulations, the less use information has to be gathered or guessed at by the regulatory exposure assessor. The value of use information is further determined by the scope of the assessment. A very conservative first tier (screening) assessment requires little information, whereas a detailed assessment of actual risks requires accurate use information. The scientific knowledge and available models limit the benefits of use information. Interesting use information has little value if there are no methods to account for the information in the exposure assessment, e.g. if relations between possible exposure determinants and exposure levels are unknown. The need for use information will further be dependent on the analysis of costs (of gathering data) versus benefits (increase in quality of the assessment). Many choices regarding the need for use information depend on policy choices by decision-makers. These decision-makers again depend on proper scientific information to make informed choices. [ABSTRACT FROM PUBLISHER]