Toward better exposure assessment strategies--the new NIOSH initiative

Direct-reading instruments for aerosols: A review for occupational health and safety professionals part 2: Applications

Direct reading instruments (DRIs) for aerosols have been used in industrial hygiene practice for many years, but their potential has not been fully realized by many occupational health and safety professionals. Although some DRIs quantify other metrics, this article will primarily focus on DRIs that measure aerosol number, size, or mass. This review addresses three applications of aerosol DRIs that occupational health and safety professionals can use to discern, characterize, and document exposure conditions and resolve aerosol-related problems in the workplace. The most common application of aerosol DRIs is the evaluation of engineering controls. Examples are provided for many types of workplaces and situations including construction, agriculture, mining, conventional manufacturing, advanced manufacturing (nanoparticle technology and additive manufacturing), and non-industrial sites. Aerosol DRIs can help identify the effectiveness of existing controls and, as needed, develop new strategies to reduce potential aerosol exposures. Aerosol concentration mapping (ACM) using DRI data can focus attention on emission sources in the workplace spatially illustrate the effectiveness of controls and constructively convey concerns to management and workers. Examples and good practices of ACM are included. Video Exposure Monitoring (VEM) is another useful technique in which video photography is synced with the concentration output of an aerosol DRI. This combination allows the occupational health and safety professional to see what tasks, environmental situations, and/or worker actions contribute to aerosol concentration and potential exposure. VEM can help identify factors responsible for temporal variations in concentration. VEM can assist with training, engage workers, convince managers about necessary remedial actions, and provide for continuous improvement of the workplace environment. Although using DRIs for control evaluation, ACM and VEM can be time-consuming, the resulting information can provide useful data to prompt needed action by employers and employees. Other barriers to adoption include privacy and security issues in some worksites. This review seeks to provide information so occupational health and safety professionals can better understand and effectively use these powerful applications of aerosol DRIs.

Direct-reading instruments for aerosols: A review for occupational health and safety professionals part 1: Instruments and good practices

With advances in technology, there are an increasing number of direct-reading instruments available to occupational health and safety professionals to evaluate occupational aerosol exposures. Despite the wide array of direct-reading instruments available to professionals, the adoption of direct-reading technology to monitor workplace exposures has been limited, partly due to a lack of knowledge on how the instruments operate, how to select an appropriate instrument, and challenges in data analysis techniques. This paper presents a review of direct-reading aerosol instruments available to occupational health and safety professionals, describes the principles of operation, guides instrument selection based on the workplace and exposure, and discusses data analysis techniques to overcome these barriers to adoption. This paper does not cover all direct-reading instruments for aerosols but only those that an occupational health and safety professional could use in a workplace to evaluate exposures. Therefore, this paper focuses on instruments that have the most potential for workplace use due to their robustness, past workplace use, and price with regard to return on investment. The instruments covered in this paper include those that measure aerosol number concentration, mass concentration, and aerosol size distributions.

The WGD-A Dataset of Assembly Line Working Gestures for Ergonomic Analysis and Work-Related Injuries Prevention

This paper wants to stress the importance of human movement monitoring to prevent musculoskeletal disorders by proposing the WGD—Working Gesture Dataset, a publicly available dataset of assembly line working gestures that aims to be used for worker’s kinematic analysis. It contains kinematic data acquired from healthy subjects performing assembly line working activities using an optoelectronic motion capture system. The acquired data were used to extract quantitative indicators to assess how the working tasks were performed and to detect useful information to estimate the exposure to the factors that may contribute to the onset of musculoskeletal disorders. The obtained results demonstrate that the proposed indicators can be exploited to early detect incorrect gestures and postures and, consequently to prevent work-related disorders. The approach is general and independent on the adopted motion analysis system. It wants to provide indications for safely performing working activities. For example, the proposed WGD can also be used to evaluate the kinematics of workers in real working environments thanks to the adoption of unobtrusive measuring systems, such as wearable sensors through the extracted indicators and thresholds.

Occupational Exposure to Mycotoxins-Different Sampling Strategies Telling a Common Story Regarding Occupational Studies Performed in Portugal (2012-2020)

In occupational settings where exposure to organic dust occurs (e.g., intensive animal production, waste management, farming and many others) workers can also be exposed to mycotoxins. However, recognizing exposure to mycotoxins in workplace environments does not happen commonly and, consequently, remains as a not identified occupational risk factor. In the last decade, work developed in different occupational settings, using different sampling approaches reported that occupational exposure to mycotoxins occurs and it's of upmost importance to be seen as an occupational concern that needs to be tackled. This paper intends to discuss the several possibilities available for assessing and characterizing the occupational exposure to mycotoxins through the description of the advantages and limitations of the different sampling strategies. Overviewing the approaches and the main achievements used in several field campaigns developed in Portugal, the knowledge obtained will be used to support the identification of the main aspects to consider when designing new occupational studies. The need for additional research work will also be discussed where new directions to follow will be debated.

Advanced REACH Tool: a Bayesian model for occupational exposure assessment

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

Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis

For more than a decade, the integration of human and environmental risk assessment (RA) has become an attractive vision. At the same time, existing European regulations of chemical substances such as REACH (EC Regulation No. 1907/2006), the Plant Protection Products Regulation (EC regulation 1107/2009) and Biocide Regulation (EC Regulation 528/2012) continue to ask for sector-specific RAs, each of which have their individual information requirements regarding exposure and hazard data, and also use different methodologies for the ultimate risk quantification. In response to this difference between the vision for integration and the current scientific and regulatory practice, the present paper outlines five medium-term opportunities for integrating human and environmental RA, followed by detailed discussions of the associated major components and their state of the art. Current hazard assessment approaches are analyzed in terms of data availability and quality, and covering non-test tools, the integrated testing strategy (ITS) approach, the adverse outcome pathway (AOP) concept, methods for assessing uncertainty, and the issue of explicitly treating mixture toxicity. With respect to exposure, opportunities for integrating exposure assessment are discussed, taking into account the uncertainty, standardization and validation of exposure modeling as well as the availability of exposure data. A further focus is on ways to complement RA by a socio-economic assessment (SEA) in order to better inform about risk management options. In this way, the present analysis, developed as part of the EU FP7 project HEROIC, may contribute to paving the way for integrating, where useful and possible, human and environmental RA in a manner suitable for its coupling with SEA.

Modelling occupational inhalation exposure to concentration peaks of chemicals and associated health risk assessment

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.

Harmonization of measurement strategies for exposure to manufactured nano-objects; report of a workshop

The present paper summarizes the outcome of the discussions at the First International Scientific Workshop on Harmonization of Strategies to Measure and Analyze Exposure to (Manufactured) Nano-objects in Workplace Air that was organized and hosted by the Netherlands Organization for Applied Scientific Research (TNO) and the Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA) (Zeist, The Netherlands, December 2010). It reflects the discussions by 25 international participants in the area of occupational (nano) exposure assessment from Europe, USA, Japan, and Korea on nano-specific issues related to the three identified topics: (i) measurement strategies; (ii) analyzing, evaluating, and reporting of exposure data; and (iii) core information for (exposure) data storage. Preliminary recommendations were achieved with respect to (i) a multimetric approach to exposure assessment, a minimal set of data to be collected, and basic data analysis and reporting as well as (ii) a minimum set of contextual information to be collected and reported. Other issues that have been identified and are of great interest include (i) the need for guidance on statistical approaches to analyze time-series data and on electron microscopy analysis and its reporting and (ii) the need for and possible structure of a (joint) database to store and merge data. To make progress in the process of harmonization, it was concluded that achieving agreement among researchers on the preliminary recommendations of the workshop is urgent.

Desktop study of occupational exposure judgments: do education and experience influence accuracy?

This study examines the impact of several experience and education determinants on exposure judgment accuracy. The study used desktop assessments performed on several different tasks with different exposure profiles to identify correlations between determinants and judgment accuracy using logistic regression models. The exposure judgments were elicited from industrial hygienists with varying levels of experience, education, and training. Videos and written and oral information about the exposure tasks were presented to all participants as they documented a series of qualitative and quantitative exposure judgment probabilities in four exposure categories. Participants (n = 77) first documented their qualitative and then their quantitative exposure assessments after receiving the series of sampling data points. Data interpretation tests and training in simple rules-of-thumb for data interpretation were also given to each participant to investigate the impact of data interpretation skills on exposure judgment accuracy. Logistic regression analysis indicated "years of exposure assessment experience" (p < 0.05), "highest EHS degree" (p < 0.05), and a participant's "data interpretation test score" (p < 0.05) directly impacted qualitative exposure judgment accuracy. Logistic regression models of quantitative judgment accuracy showed positive correlation with "greater than 10 years of exposure assessment experience" (p < 0.05), "highest EHS degree" (p < 0.05), a participant's "data interpretation test score" (p < 0.001), rules-of-thumb data interpretation training (p < 0.001), and the number of sample data points available for a judgment (p < 0.005). Analyzing judgments in subsets for participants with less or more than 10 years' experience indicated additional correlations with Certified Industrial Hygienist and Certified Safety Professional certifications, total number of task exposure assessments, and career number of air surveys. The correlation of qualitative and quantitative exposure judgment accuracy with "greater than 10 years experience" supports similar research findings from other fields. The results of this study indicate that several determinants of experience, education, and training, in addition to the availability of sampling data, significantly impact the accuracy of exposure assessments. The findings also suggest methods for enhancing exposure judgment accuracy through statistical tools, mathematical exposure modeling, and specific training.

Refinement and validation of an exposure model for the pharmaceutical industry

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Bayesian modeling of exposure and airflow using two-zone models

Mathematical modeling is being increasingly used as a means for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Validation of models in occupational settings is, therefore, a challenge. Not only do the model parameters need to be known, the models also need to predict the output with some degree of accuracy. In this paper, a Bayesian statistical framework is used for estimating model parameters and exposure concentrations for a two-zone model. The model predicts concentrations in a zone near the source and far away from the source as functions of the toluene generation rate, air ventilation rate through the chamber, and the airflow between near and far fields. The framework combines prior or expert information on the physical model along with the observed data. The framework is applied to simulated data as well as data obtained from the experiments conducted in a chamber. Toluene vapors are generated from a source under different conditions of airflow direction, the presence of a mannequin, and simulated body heat of the mannequin. The Bayesian framework accounts for uncertainty in measurement as well as in the unknown rate of airflow between the near and far fields. The results show that estimates of the interzonal airflow are always close to the estimated equilibrium solutions, which implies that the method works efficiently. The predictions of near-field concentration for both the simulated and real data show nice concordance with the true values, indicating that the two-zone model assumptions agree with the reality to a large extent and the model is suitable for predicting the contaminant concentration. Comparison of the estimated model and its margin of error with the experimental data thus enables validation of the physical model assumptions. The approach illustrates how exposure models and information on model parameters together with the knowledge of uncertainty and variability in these quantities can be used to not only provide better estimates of model outputs but also model parameters.