Exposure Monitoring Strategies for Applying Low-Cost PM Sensors to Assess Flour Dust in Industrial Bakeries.

Low-cost particulate matter (PM) sensors provide new methods for monitoring occupational exposure to hazardous substances, such as flour dust. These devices have many possible benefits, but much remains unknown about their performance for different exposure monitoring strategies in the workplace. We explored the performance of PM sensors for four different monitoring strategies (time-weighted average and high time resolution, each quantitative and semi-quantitative) for assessing occupational exposure using low-cost PM sensors in a field study in the industrial bakery sector. Measurements were collected using four types of sensor (PATS+, Isensit, Airbeam2, and Munisense) and two reference devices (respirable gravimetric samplers and an established time-resolved device) at two large-scale bakeries, spread over 11 participants and 6 measurement days. Average PM2.5 concentrations of the low-cost sensors were compared with gravimetric respirable concentrations for 8-h shift periods and 1-min PM2.5 concentrations of the low-cost sensors were compared with time-resolved PM2.5 data from the reference device (quantitative monitoring strategy). Low-cost sensors were also ranked in terms of exposure for 8-h shifts and for 15-min periods with a shift (semi-quantitative monitoring strategy). Environmental factors and methodological variables, which can affect sensor performance, were investigated. Semi-quantitative monitoring strategies only showed more accurate results compared with quantitative strategies when these were based on shift-average exposures. The main factors that influenced sensor performance were the type of placement (positioning the devices stationary versus personal) and the company or workstation where measurements were collected. Together, these findings provide an overview of common strengths and drawbacks of low-cost sensors and different ways these can be applied in the workplace. This can be used as a starting point for further investigations and the development of guidance documents and data analysis methods.

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

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

Exploring Evaluation Variables for Low-Cost Particulate Matter Monitors to Assess Occupational Exposure.

(1) Background: Small, lightweight, low-cost optical particulate matter (PM) monitors are becoming popular in the field of occupational exposure monitoring, because these devices allow for real-time static measurements to be collected at multiple locations throughout a work site as well as being used as wearables providing personal exposure estimates. Prior to deployment, devices should be evaluated to optimize and quantify measurement accuracy. However, this can turn out to be difficult, as no standardized methods are yet available and different deployments may require different evaluation procedures. To gain insight in the relevance of different variables that may affect the monitor readings, six PM monitors were selected based on current availability and evaluated in the laboratory; (2) Methods: Existing strategies that were judged appropriate for the evaluation of PM monitors were reviewed and seven evaluation variables were selected, namely the type of dust, within- and between-device variations, nature of the power supply, temperature, relative humidity, and exposure pattern (peak and constant). Each variable was tested and analyzed individually and, if found to affect the readings significantly, included in a final correction model specific to each monitor. Finally, the accuracy for each monitor after correction was calculated; (3) Results: The reference materials and exposure patterns were found to be main factors needing correction for most monitors. One PM monitor was found to be sufficiently accurate at concentrations up to 2000 µg/m3 PM2.5, with other monitors appropriate at lower concentrations. The average accuracy increased by up to three-fold compared to when the correction model did not include evaluation variables; (4) Conclusions: Laboratory evaluation and readings correction can greatly increase the accuracy of PM monitors and set boundaries for appropriate use. However, this requires identifying the relevant evaluation variables, which are heavily reliant on how the monitors are used in the workplace. This, together with the lack of current consensus on standardized procedures, shows the need for harmonized PM monitor evaluation methods for occupational exposure monitoring.

Classification of occupational activities for assessment of inhalation exposure.

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

Conceptual model for assessment of inhalation exposure: defining modifying factors.

The present paper proposes a source-receptor model to schematically describe inhalation exposure to help understand the complex processes leading to inhalation of hazardous substances. The model considers a stepwise transfer of a contaminant from the source to the receptor. The conceptual model is constructed using three components, i.e. (i) the source, (ii) various transmission compartments and (iii) the receptor, and describes the contaminant's emission and its pattern of transport. Based on this conceptual model, a list of nine mutually independent principal modifying factors (MFs) is proposed: activity emission potential, substance emission potential, localized control, separation, segregation, dilution, worker behavior, surface contamination and respiratory protection. These MFs describe the exposure process at a high level of abstraction so that the model can be generically applicable. A list of exposure determinants underlying each of these principal MFs is proposed to describe the exposure process at a more detailed level. The presented conceptual model is developed in conjunction with an activity taxonomy as described in a separate paper. The proposed conceptual model and MFs should be seen as 'building blocks' for development of higher tier exposure models.

Tools for regulatory assessment of occupational exposure: development and challenges.

REACH (Registration, Evaluation and Authorization of CHemicals) requires improved exposure models that can be incorporated into screening tools and refined assessment tools. These are referred to as tier 1 and 2 models, respectively. There are a number of candidate in tier 1 models that could be used with REACH. Tier 2 models, producing robust and realistic exposure assessments, are currently not available. A research programme is proposed in this paper that will result in a new, advanced exposure assessment tool for REACH. In addition, issues related to variability and uncertainty are discussed briefly, and some examples of tier 1 screening tools are presented. The proposed framework for the tier 2 tool is based on a Bayesian approach, and makes full use of mechanistically modelled estimates and any relevant measurements of exposure. The new approach will preclude the necessity to conduct of case-by-case exposure measurements for each chemical and scenario, since the system will allow for the use of analogous exposure data from relatively comparable scenarios. The development of the new approach requires substantial effort in the area of mechanistic modelling, database development and Bayesian statistical techniques. In this paper, the data gaps and areas for future research are identified to help realise and further improve this type of approach within REACH. A structured data collection and storage system is a central element of the research programme and the availability of this type of tool may also facilitate the sharing of exposure data down and up the supply chain. In addition, new data that are stored according to the proposed structure could enable the validation of any exposure model and thus this programme enhances the exposure assessment field as a whole.