Influence of analysis methods on interpretation of hazard maps

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.

Combining physics-based and Kriging models to improve the estimation of noise exposure

Worker exposure to occupational hazards is traditionally measured by equipping workers with wearable exposure monitors. An emerging alternative measurement first generates time-varying hazard maps from permanent monitors within the facility, then estimating worker exposure by integrating hazard levels traversed in map, following the tracked movement of workers. Complex environments may require many monitors to produce a hazard map with the necessary accuracy, but effective interpolation functions can reduce the required number of monitors needed. This work assesses the effectiveness of three models for accurately interpolating hazard levels among monitors: a traditional Kriging model, a physics-based model, and a hybrid model that combines the Kriging and physics-based models. The effectiveness of each interpolation function was tested with sound levels collected in four environmental settings. These detailed experimental data were used to generate over 10,000 simulation trials, where each trial configured the experimental data into a unique arrangement of simulated monitoring and sampling positions. For each simulation trial, the effectiveness of the three models was assessed with the root mean square error of the sound levels at the simulated sampling positions, using the simulated monitoring positions as input. The interpolated values between the monitored positions were analyzed separately from the extrapolated values beyond the monitored positions. The hybrid model consistently reported among the lowest errors in each trial. The Kriging model performed best for the densest networks (those with the most monitors). Even in these cases, the hybrid model performed within 10% of the Kriging model with less than a third of the monitors. The experiment demonstrates that the hybrid model is highly effective at estimating hazardous sound levels; future work may demonstrate similar advantages for gas and aerosol hazards.

Optimizing a Sensor Network with Data from Hazard Mapping Demonstrated in a Heavy-Vehicle Manufacturing Facility

Objectives

To design a method that uses preliminary hazard mapping data to optimize the number and location of sensors within a network for a long-term assessment of occupational concentrations, while preserving temporal variability, accuracy, and precision of predicted hazards.

Methods

Particle number concentrations (PNCs) and respirable mass concentrations (RMCs) were measured with direct-reading instruments in a large heavy-vehicle manufacturing facility at 80-82 locations during 7 mapping events, stratified by day and season. Using kriged hazard mapping, a statistical approach identified optimal orders for removing locations to capture temporal variability and high prediction precision of PNC and RMC concentrations. We compared optimal-removal, random-removal, and least-optimal-removal orders to bound prediction performance.

Results

The temporal variability of PNC was found to be higher than RMC with low correlation between the two particulate metrics (ρ = 0.30). Optimal-removal orders resulted in more accurate PNC kriged estimates (root mean square error [RMSE] = 49.2) at sample locations compared with random-removal order (RMSE = 55.7). For estimates at locations having concentrations in the upper 10th percentile, the optimal-removal order preserved average estimated concentrations better than random- or least-optimal-removal orders (P < 0.01). However, estimated average concentrations using an optimal-removal were not statistically different than random-removal when averaged over the entire facility. No statistical difference was observed for optimal- and random-removal methods for RMCs that were less variable in time and space than PNCs.

Conclusions

Optimized removal performed better than random-removal in preserving high temporal variability and accuracy of hazard map for PNC, but not for the more spatially homogeneous RMC. These results can be used to reduce the number of locations used in a network of static sensors for long-term monitoring of hazards in the workplace, without sacrificing prediction performance.

Mapping Occupational Hazards with a Multi-sensor Network in a Heavy-Vehicle Manufacturing Facility

Due to their small size, low-power demands, and customizability, low-cost sensors can be deployed in collections that are spatially distributed in the environment, known as sensor networks. The literature contains examples of such networks in the ambient environment; this article describes the development and deployment of a 40-node multi-hazard network, constructed with low-cost sensors for particulate matter (SHARP GP2Y1010AU0F), carbon monoxide (Alphasense CO-B4), oxidizing gases (Alphasense OX-B421), and noise (developed in-house) in a heavy-vehicle manufacturing facility. Network nodes communicated wirelessly with a central database in order to record hazard measurements at 5-min intervals. Here, we report on the temporal and spatial measurements from the network, precision of network measurements, and accuracy of network measurements with respect to field reference instruments through 8 months of continuous deployment. During typical production periods, 1-h mean hazard levels ± standard deviation across all monitors for particulate matter (PM), carbon monoxide (CO), oxidizing gases (OX), and noise were 0.62 ± 0.2 mg m-3, 7 ± 2 ppm, 155 ± 58 ppb, and 82 ± 1 dBA, respectively. We observed clear diurnal and weekly temporal patterns for all hazards and daily, hazard-specific spatial patterns attributable to general manufacturing processes in the facility. Processes associated with the highest hazard levels were machining and welding (PM and noise), staging (CO), and manual and robotic welding (OX). Network sensors exhibited varying degrees of precision with 95% of measurements among three collocated nodes within 0.21 mg m-3 for PM, 0.4 ppm for CO, 9 ppb for OX, and 1 dBA for noise of each other. The median percent bias with reference to direct-reading instruments was 27%, 11%, 45%, and 1%, for PM, CO, OX, and noise, respectively. This study demonstrates the successful long-term deployment of a multi-hazard sensor network in an industrial manufacturing setting and illustrates the high temporal and spatial resolution of hazard data that sensor and monitor networks are capable of. We show that network-derived hazard measurements offer rich datasets to comprehensively assess occupational hazards. Our network sets the stage for the characterization of occupational exposures on the individual level with wireless sensor networks.

Low-Cost, Distributed Environmental Monitors for Factory Worker Health

An integrated network of environmental monitors was developed to continuously measure several airborne hazards in a manufacturing facility. The monitors integrated low-cost sensors to measure particulate matter, carbon monoxide, ozone and nitrogen dioxide, noise, temperature and humidity. The monitors were developed and tested in situ for three months in several overlapping deployments, before a full cohort of 40 was deployed in a heavy vehicle manufacturing facility for a year of data collection. The monitors collect data from each sensor and report them to a central database every 5 min. The work includes an experimental validation of the particle, gas and noise monitors. The R² for the particle sensor ranges between 0.98 and 0.99 for particle mass densities up to 300 μg/m³. The R² for the carbon monoxide sensor is 0.99 for concentrations up to 15 ppm. The R² for the oxidizing gas sensor is 0.98 over the sensitive range from 20 to 180 ppb. The noise monitor is precise within 1% between 65 and 95 dBA. This work demonstrates the capability of distributed monitoring as a means to examine exposure variability in both space and time, building an important preliminary step towards a new approach for workplace hazard monitoring.

STATIC AND ROVING SENSOR DATA FUSION FOR SPATIO-TEMPORAL HAZARD MAPPING WITH APPLICATION TO OCCUPATIONAL EXPOSURE ASSESSMENT

Rapid technological advances have drastically improved the data collection capacity in occupational exposure assessment. However, advanced statistical methods for analyzing such data and drawing proper inference remain limited. The objectives of this paper are (1) to provide new spatio-temporal methodology that combines data from both roving and static sensors for data processing and hazard mapping across space and over time in an indoor environment, and (2) to compare the new method with the current industry practice, demonstrating the distinct advantages of the new method and the impact on occupational hazard assessment and future policy making in environmental health as well as occupational health. A novel spatio-temporal model with a continuous index in both space and time is proposed, and a profile likelihood-based model fitting procedure is developed that allows fusion of the two types of data. To account for potential differences between the static and roving sensors, we extend the model to have nonhomogenous measurement error variances. Our methodology is applied to a case study conducted in an engine test facility, and dynamic hazard maps are drawn to show features in the data that would have been missed by existing approaches, but are captured by the new method.

Effects of data sparsity and spatiotemporal variability on hazard maps of workplace noise

Personal sampling, considered a state-of-the-art technique to assess worker exposures to occupational hazards, is often conducted for the duration of a work shift so that time-weighted average (TWA) exposures may be evaluated relative to published occupational exposure limits (OELs). Such cross-shift measurements, however, provide little information on the spatial variability of exposures, except after a very large number of samples. Hazard maps, contour plots (or similar depiction) of hazard intensity throughout the workplace, have gained popularity as a way to locate sources and to visualize spatial variability of physical and chemical hazards within a facility. However, these maps are often generated from short duration measures and have little ability to assess temporal variability. To assess the potential bias that results from the use of short-duration measurements to represent the TWA in a hazard map, noise intensity measurements were collected at high spatial and temporal resolution in two facilities. Static monitors were distributed throughout the facility and used to capture the temporal variability at these locations. Roving monitors (typical of the hazard mapping process) captured spatial variability over multiple traverses through the facility. The differences in hazards maps generated with different sampling techniques were evaluated. Hazard maps produced from sparse, roving monitor data were in good agreement with the TWA hazard maps at the facility with low temporal variability. Estimated values were within 5 dB of the TWA over approximately 90% of the facility. However, at the facility with higher temporal variability, large differences between hazard maps were observed for different traverses through the facility. On the second day of sampling, estimates were at least 5 dB different than the TWA for more than half of the locations within the facility. The temporal variability of noise was found to have a greater influence on map accuracy than the spatial sampling resolution.