A worker’s personal exposure to respirable dust in occupational environments has traditionally been monitored using established methodologies which entail the collection of an 8-h representative sample that is sent away for laboratory analysis. While these methods are very accurate, they only provide information on the average exposure during a specific time period, generally a worker’s shift. The availability of relatively inexpensive aerosol sensors can allow researchers and practitioners to generate real-time data with unprecedented spatial and temporal granularity. Low-cost dust monitors (LCDM) were developed and marketed for air pollution monitoring and are mostly being used to help communities understand their local and even hyper-local air quality. Most of these integrated sensing packages cost less than $300 per unit, in contrast to wearable or area dust monitors specifically built for mining applications which have been around for decades but still average around $5000 each. At the National Institute for Occupational Safety and Health (NIOSH), we are leveraging the power of high-volume data collection from networks of LCDM to establish baseline respirable hazard levels and to monitor for changes on a seasonal basis as well as following any application of control technologies. We have seen the effective use and advantages of monitoring live data before, during, and after events like shift changes, operational changes, ventilation upgrades, adverse weather events, and machine maintenance. However, many factors have prevented a systematic adoption of LCDMs for exposure monitoring: concern for their analytical performance, the complexity of use, and lack of understanding of their value are some factors. This contribution outlines a 1-year case study at a mine in Wisconsin, USA, covering the installation, maintenance, data visualizations, and collaboration between NIOSH researchers and the industrial hygiene professionals at the mine. © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024.

With the advent of new sensing technologies and robust field-deployable analyzers, monitoring approaches can now generate valuable hazard information directly in the workplace. This is the case for monitoring respirable dust and respirable crystalline silica concentration levels. Estimating the quartz amount of a respirable dust sample by nondestructive analysis can be carried out using portable Fourier transform infrared spectroscopy (FTIR) units. Real-time respirable dust monitors, combined with small video cameras, allow advanced assessments using the Helmet-CAM methodology. These two field-based monitoring approaches, developed by the National Institute for Occupational Safety and Health (NIOSH), have been trialed in a sandstone quarry. Twenty-six Helmet-CAM sessions were conducted, and forty-one dust samples were collected around the quarry and analyzed on site during two events. The generated data generated were used to characterize concentration levels for the monitored areas and workers, to identify good practices, and to illustrate activities that could be improved with additional engineered control technologies. Laboratory analysis on the collected samples complemented the field finding and provided an assessment of the performance of the field-based techniques. Only a fraction of the real-time respirable dust monitoring sessions data could be corrected with laboratory analysis. The average correction factor ratio was 5.0. Nevertheless, Helmet-CAM results provided valuable information for each session. The field-based quartz monitoring approach overestimated the concentration by a factor of 1.8, but it successfully assessed the quartz concentration trends in the quarry. The data collected could be used for the determination of a quarry calibration factor for future events. The quartz content in the dust was found to vary from 14% to 100%, and this indicates the need for multiple techniques in the characterization of respirable dust and quartz concentration and exposure. Overall, this study reports the importance of the adoption of field-based monitoring techniques when combined with a proper understanding and knowledge of the capabilities and limitations of each technique.

In the ever-expanding complexities of the modern-day mining workplace, the continual monitoring of a safe and healthy work environment is a growing challenge. One specific workplace exposure concern is the inhalation of dust containing respirable crystalline silica (RCS) which can lead to silicosis, a potentially fatal lung disease. This is a recognized and regulated health hazard, commonly found in mining. The current methodologies to monitor this type of exposure involve distributed sample collection followed by costly and relatively lengthy follow-up laboratory analysis. To address this concern, we have investigated a data-driven predictive modeling pipeline to predict the amount of silica deposition quickly and accurately on a filter within minutes of sample collection completion. This field-based silica monitoring technique involves the use of small, and easily deployable, Fourier transform infrared (FTIR) spectrometers used for data collection followed by multivariate regression methodologies including Principal Component Analysis (PCA) and Partial Least Squares (PLS). Given the complex nature of respirable dust mixtures, there is an increasing need to account for multiple variables quickly and efficiently during analysis. This analysis consists of several quality control steps including data normalization, PCA and PLS outlier detection, as well as applying correction factors based on the sampler and cassette used for sample collection. While outside the scope of this article to test, these quality control steps will allow for the acceptance of data from many different FTIR instruments and sampling types, thus increasing the overall useability of this method. Additionally, any sample analyzed through the model and validated using a secondary method can be incorporated into the training dataset creating an ever-growing, more robust predictive model. Multivariant predictive modeling has far-reaching implications given its speed, cost, and scalability compared to conventional approaches. This contribution presents the application of PCA and PLS as part of a computational pipeline approach to predict the amount of a deposited mineral of interest using FTIR data. For this specific application, we have developed the model to analyze RCS, although this process can be implemented in the analysis of any IR-active mineral, and this pipeline applied to any FTIR data.

Canopy air curtains on roof bolting machines have been proven to protect miners from respirable dust, preventing their overexposure to dust. Another desired application for canopy air curtains is in the compartments of shuttle cars. The challenges faced in developing the design of canopy air curtains for shuttle cars include mine ventilation rates in tandem with the shuttle car tram speeds. The resulting cab airspeeds may exceed 182 m/min (600 fpm), as found in the present study conducted in a central Appalachian underground coal mine by U.S. National Institute for Occupational Safety and Health (NIOSH) researchers. Prior research and laboratory testing had indicated that successfully protecting a miner in high air velocities is difficult, because the clean air from the canopy air curtain is unable to penetrate through the high-velocity mine air. In this study, the dust concentrations to which a shuttle car operator was exposed were measured, and air velocities experienced by the operator were measured as well using a recording vane anemometer. The results indicate that the highest exposure to respirable dust, 2.22 mg/m3, occurred when the shuttle car was loading at the continuous miner, where the average airspeed was 48 m/min (157 fpm). While tramming, the operator was exposed to 0.77 mg/m3 of respirable dust with an average airspeed of 62 m/min (203 fpm). This study indicates that a canopy air curtain system can be designed to greatly reduce an operator's exposure to respirable dust by providing clean air to the operator, as the majority of the operator's dust exposure occurs in air velocities slower than 61 m/min (200 fpm).

Canopy air curtain (CAC) technology has been developed by the National Institute for Occupational Safety and Health (NIOSH) for use on continuous miners and subsequently roof bolting machines in underground coal mines to protect operators of these machines from overexposure to respirable coal mine dust. The next logical progression is to develop a CAC for shuttle cars to protect operators from the same overexposures. NIOSH awarded a contract to Marshall University and J.H. Fletcher to develop the shuttle car CAC. NIOSH conducted laboratory testing to determine the dust control efficiency of the shuttle car CAC. Testing was conducted on two different cab configurations: a center drive similar to that on a Joy 10SC32AA cab model and an end drive similar to that on a Joy 10SC32AB cab model. Three different ventilation velocities were tested-0.61, 2.0, 4.3 m/s (120, 400, and 850 fpm). The lowest, 0.61 m/s (120 fpm), represented the ventilation velocity encountered during loading by the continuous miner, while the 4.3 m/s (850 fpm) velocity represented ventilation velocity airflow over the shuttle car while tramming against ventilation airflow. Test results showed an average of the dust control efficiencies ranging from 74 to 83% for 0.61 m/s (120 fpm), 39%-43% for 2.0 m/s (400 fpm), and 6%-16% for 4.3 m/s (850 fpm). Incorporating an airflow spoiler to the shuttle car CAC design and placing the CAC so that it is located 22.86 cm (9 in.) forward of the operator improved the dust control efficiency to 51%-55% for 4.3 m/s (850 fpm) with minimal impact on dust control efficiencies for lower ventilation velocities. These laboratory tests demonstrate that the newly developed shuttle car CAC has the potential to successfully protect shuttle car operators from coal mine respirable dust overexposures.