Mine Safety And Health Administration Research Articles

Analysis of the root causes of fatal injuries in the United States surface mines between 2008 and 2021

ABSTRACT Despite significant reductions in fatal injuries in the mining industry, the number of accidents remains unacceptable. Ongoing concern persists regarding mine equipment. This study analyzes the Mine Safety and Health Administration (MSHA) data from 241 final reports of fatal injuries in U.S. surface mines between 2008 and 2021. Predominantly, powered-haulage was the leading source of fatalities; novice miners were particularly vulnerable, and the most significant root cause group was ‘Failure to follow safe work procedures and conditions while performing the task’. Results should heighten hazard awareness for industry professionals, helping them identify and prioritise safety issues in surface mining systems.

Using Machine Learning to Evaluate Coal Geochemical Data with Respect to Dynamic Failures

Dynamic failure events have occurred in the underground coal mining industry since its inception. Recent NIOSH research has identified geochemical markers that correlate with in situ reportable dynamic event occurrence, although the causes behind this correlative relationship remain unclear. In this study, NIOSH researchers conducted machine learning analysis to examine whether a model could be constructed to assess the probability of dynamic failure occurrence based on geochemical and petrographic data. Linear regression, random forest, dimensionality reduction, and cluster analyses were applied to a catalog of dynamic failure and control data from the Pennsylvania Coal Sample Databank, cross-referenced with accident data from the Mine Safety and Health Administration (MSHA). Analyses determined that 7 of the 18 geochemical parameters that were examined had the biggest impact on model performance. Classifications based on logistic regression and random forest models attained precision values of 85.7% and 96.7%, respectively. Dimensionality reduction was used to explore patterns and groupings in the data and to search for relationships between compositional parameters. Cluster analyses were performed to determine if an algorithm could find clusters with given class memberships and to what extent misclassifications of dynamic failure status occurred. Cluster analysis using a hierarchal clustering algorithm after dimensionality reduction resulted in four clusters, with one relatively distinct dynamic failure cluster, and three clusters mostly consisting of control group members but with a small number of dynamic failure members.

Contextual Representation in NLP to Improve Success in Accident Classification of Mine Safety Narratives

Contextual representation has taken center stage in Natural Language Processing (NLP) in the recent past. Models such as Bidirectional Encoder Representations from Transformers (BERT) have found tremendous success in the arena. As a first attempt in the mining industry, in the current work, BERT architecture is adapted in developing the MineBERT model to accomplish the classification of accident narratives from the US Mine Safety and Health Administration (MSHA) data set. In the past multi-year research, several machine learning (ML) methods were used by authors to improve classification success rates in nine significant MSHA accident categories. Out of nine, for four major categories (“Type Groups”) and five “narrow groups”, Random Forests (RF) registered 75% and 42% classification success rates, respectively, on average, while keeping the false positives under 5%. Feature-based innovative NLP methods such as accident-specific expert choice vocabulary (ASECV) and similarity score (SS) methods were developed to improve upon the RF success rates. A combination of all these methods (“Stacked” approach) is able to slightly improve success over RF (71%) to 73.28% for the major category “Caught-in”. Homographs in narratives are identified as the major problem that was preventing further success. Their presence was creating ambiguity problems for classification algorithms. Adaptation of BERT effectively solved the problem. When compared to RF, MineBERT implementation improved success rates among major and narrow groups by 13% and 32%, respectively, while keeping the false positives under 1%, which is very significant. However, BERT implementation in the mining industry, which has unique technical aspects and jargon, brought a set of challenges in terms of preparation of data, selection of hyperparameters, and fine-tuning the model to achieve the best performance, which was met in the current research.

Alterations to biomarkers related to long-term exposure to diesel exhaust at concentrations below occupational exposure limits in the European Union and the USA

BackgroundWe previously found that occupational exposure to diesel engine exhaust (DEE) was associated with alterations to 19 biomarkers that potentially reflect the mechanisms of carcinogenesis. Whether DEE is associated with...

Poor adherence to dust, noise and safety regulations predict injury rates in underground coal mines

BackgroundWhile safety in US coal mining has improved over the past two decades, general occupational health research shows that risk of injury varies across individual worksites and is influenced by...

Occupational exposure to respirable crystalline silica among US metal and nonmetal miners, 2000-2019.

In metal and nonmetal (M/NM) mines in the United States, respirable crystalline silica (RCS) exposures are a recognized health hazard and a leading indicator of respiratory disease. This study describes hazardous exposures that exceed occupational exposure limits and examines patterns of hazardous RCS exposure over time among M/NM miners to better inform the need for interventions. Data for this study were obtained from the Mine Safety and Health Administration (MSHA) Open Government Initiative Portal for the years 2000-2019, examining respirable dust samples with MSHA-measured quartz concentration >1%. Descriptive statistics for RCS were analyzed for M/NM miners by year, mine type, sector, commodity, occupation, and location in a mine. This study found the overall geometric mean (GM) for personal exposures to RCS was 28.9 μg/m3 (geometric standard deviation: 2.5). Exposures varied significantly by year, mine type, sector, commodity, occupation, and location in a mine. Overall, the percentages of exposures above the MSHA permissible exposure limit (PEL for respirable dust with >1% quartz, approximately 100 μg/m3 RCS) and the National Institute for Occupational Safety and Health RCS recommended exposure limit (REL, 50 μg/m3 ) were 11.8% and 27.3%, respectively. GM exposures to RCS in 2018 (45.9 μg/m3 ) and 2019 (52.9 μg/m3 ) were significantly higher than the GM for all years prior. The overall 95th percentile of RCS exposures from 2000 to 2019 was 148.9 μg/m3 , suggesting a substantial risk of hazardous exposures above the PEL and REL during the entire period analyzed. The prevalence of high exposures to RCS among M/NM miners continues in the past 20 years and may be increasing in certain settings and occupations. Further research and intervention of the highest exposures are needed to minimize the risks of acquiring silica-induced respiratory diseases.

Intrinsically Safe Drone Propulsion System for Underground Coal Mining Applications: Computational and Experimental Studies

The mining industry has recently shown increased interest in drones for routine activities in underground and surface mines. Designing a drone for coal mines is extremely complicated since the Mine Safety and Health Administration (MSHA) has tight guidelines for any equipment that can be used in underground coal mines. Due to these criteria, designing a drone for underground coal mining is exceedingly difficult. This paper explores the challenges of creating an intrinsically safe drone propulsion system. To address the challenges of designing an intrinsically safe drone’s propulsion system for an underground coal mine, this work aims to investigate the potential approaches to enhance efficiency and mitigate the heat. The study begins with the drone’s sizing approach before moving on to the experimental setup that is utilized to test the drone’s propulsion system. Finally, answers to numerous issues arising during the inquiry are offered, and these solutions are empirically explored.

The Importance of Specific Phrases in Automatically Classifying Mine Accident Narratives Using Natural Language Processing

The mining industry is diligent about reporting on safety incidents. However, these reports are not necessarily analyzed holistically to gain deep insights. Previously, it was demonstrated that mine accident narratives at a partner mine site could be automatically classified using natural language processing (NLP)-based random forest (RF) models developed, using narratives from the United States Mine Safety and Health Administration (MSHA) database. Classification of narratives is important from a holistic perspective as it affects safety intervention strategies. This paper continued the work to improve the RF classification performance in the category “caught in”. In this context, three approaches were presented in the paper. At first, two new methods were developed, named, the similarity score (SS) method and the accident-specific expert choice vocabulary (ASECV) method. The SS method focused on words or phrases that occurred most frequently, while the ASECV, a heuristic approach, focused on a narrow set of phrases. The two methods were tested with a series of experiments (iterations) on the MSHA narratives of accident category “caught in”. The SS method was not very successful due to its high false positive rates. The ASECV method, on the other hand, had low false positive rates. As a third approach (the “stacking” method), when a highly successful incidence (iteration) from ASECV method was applied in combination with the previously developed RF model (by stacking), the overall predictability of the combined model improved from 71% to 73.28%. Thus, the research showed that some phrases are key to describing particular (“caught in” in this case) types of accidents.

The Necessity for Improved Hand and Finger Protection in Mining.

Injuries associated with hands and fingers are highly prevalent in mining and identifying factors associated with these injuries are critical in developing prevention efforts. This study identifies nonfatal injury incidence rates, nature of injury, work activities, glove usage, and sources of hand and finger injuries in the U.S. mining industry, as reported to the Mine Safety and Health Administration (MSHA) from 2011 - 2017. Hand and finger injuries occur at a rate of 6.53 per 1000 full-time employees, which is nearly double the rate of the next highest affected body part, the back. Most of the hand and finger injuries were classified as cuts/lacerations/punctures (53%) followed by bone fractures/chips (26%). Materials handling and maintenance/repair were common activities at the time of the incident with miscellaneous metals (pipe, wire, guarding) and hand tools as the primary sources of hand and finger injury. Although the information on glove use was limited, leather gloves were most often worn when an injury occurred. When worn, gloves were found to contribute to 20% of the injuries, indicating their potential to protect the hands, but also potentially put the hands at risk. Further research is necessary to determine performance requirements for gloves used in mining operations, specifically those offering cut and puncture resistance.

An analysis of contributing mining factors in coal workers\u2019 pneumoconiosis prevalence in the United States coal mines, 1986\u20132018

In the United States, an unexpected and severe increase in coal miners’ lung diseases in the late 1990s prompted researchers to investigate the causes of the disease resurgence. This study aims to scrutinize the effects of various mining parameters, including coal rank, mine size, mine operation type, coal seam height, and geographical location on the prevalence of coal worker's pneumoconiosis (CWP) in surface and underground coal mines. A comprehensive dataset was created using the U.S. Mine Safety and Health Administration (MSHA) Employment and Accident/Injury databases. The information was merged based on the mine ID by utilizing SQL data management software. A total number of 123,589 mine-year observations were included in the statistical analysis. Generalized Estimating Equation (GEE) model was used to conduct a statistical analysis on a total of 29,707, and 32,643 mine-year observations for underground and surface coal mines, respectively. The results of the econometrics approach revealed that coal workers in underground coal mines are at a greater risk of CWP comparing to those of surface coal operations. Furthermore, underground coal mines in the Appalachia and Interior regions are at a higher risk of CWP prevalence than the Western region. Surface coal mines in the Appalachian coal region are more likely to CWP development than miners in the Western region. The analysis also indicated that coal workers working in smaller mines are more vulnerable to CWP than those in large mine sizes. Furthermore, coal workers in thin-seam underground mine operations are more likely to develop CWP.

Analysis of Shoulder Sprains and Strains in Mining

Material handling injuries reported to the U.S. Mine Safety and Health Administration (MSHA) result in nearly 70,000 days of work lost each year. Several mitigation efforts for these injuries focus on the back, but shoulder injuries account for four times the days lost. Nonfatal incidents reported to MSHA from 2013 through 2017 were limited to shoulder sprains and strains and were analyzed to determine what contributed the most to these injuries. Injuries were coded based on the task performed, motions involved, and the tools used. The analysis indicated that auto maintenance and tasks involving loading/unloading supplies led to the highest number of injuries. Many of these injuries were related to operating equipment or the use of specific tools. The injuries often involved lifting/lowering or pulling/pushing movements. These findings suggest future mitigation strategies for the risk of shoulder sprains and strains should focus on auto maintenance and tasks involving loading/unloading supplies.

Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine

To achieve the goal of preventing serious injuries and fatalities, it is important for a mine site to analyze site specific mine safety data. The advances in natural language processing (NLP) create an opportunity to develop machine learning (ML) tools to automate analysis of mine health and safety management systems (HSMS) data without requiring experts at every mine site. As a demonstration, nine random forest (RF) models were developed to classify narratives from the Mine Safety and Health Administration (MSHA) database into nine accident types. MSHA accident categories are quite descriptive and are, thus, a proxy for high level understanding of the incidents. A single model developed to classify narratives into a single category was more effective than a single model that classified narratives into different categories. The developed models were then applied to narratives taken from a mine HSMS (non-MSHA), to classify them into MSHA accident categories. About two thirds of the non-MSHA narratives were automatically classified by the RF models. The automatically classified narratives were then evaluated manually. The evaluation showed an accuracy of 96% for automated classifications. The near perfect classification of non-MSHA narratives by MSHA based machine learning models demonstrates that NLP can be a powerful tool to analyze HSMS data.

Respirable Coal Mine Dust: A Review of Respiratory Deposition, Regulations, and Characterization

In the late 1990s, despite years of efforts to understand and reduce coal worker’s pneumoconiosis (CWP) prevalence from more than 30% in 1970 to less than 4.2%, the level of occurrence among the US coal miners increased unexpectedly. The recent resurgence of lung diseases has raised concerns in the scientific and regulatory communities. In 2014, the United States Mine Safety and Health Administration (MSHA) issued a new dust rule changing the respirable coal mine dust (RCMD) exposure limits, measurement technology, and sampling protocol. The analysis for probable causes for the substantial increase in the CWP incidence rate is rather complicated. This paper aims to conduct a review of RCMD respiratory deposition, health effects, monitoring, regulations, and particle characteristics. The primary sources of RCMD along with the health risks from potential exposure are highlighted, and the current RCMD exposure regulations of the major coal producer countries are compared. A summary of RCMD characterization studies from 1972 to the present is provided. A review of the literature revealed that numerous factors, including geological and mining parameters, advancements in mining practices, particle characteristics, and monitoring approaches are considered to contribute to the recent resurgence of RCMD lung diseases. However, the root causes of the problem are still unknown. The effectiveness of the new dust rules in the United States will probably take years to be correctly assessed. Therefore, future research is needed to understand the relationship between RCMD particle characteristics and lung deposition, and the efficacy of current monitoring practices to measure the true dose of RCMD exposure.

Illustrating the Luminaire Comparison Method

The Mine Safety and Health Administration (MSHA) enforces underground coal mining lighting regulations in the United States and issues approvals for electrical devices and machines. Companies submit standard test and evaluation (ST& E) documentation to MSHA when seeking approval on a machine-mounted lighting system. Subsequent machine modifications, such as changing the type of light lamp from incandescent to light-emitting diode (LED), will require an update via the revised approval modification program (RAMP). Older incandescent and fluorescent lamps are being replaced by LED technology which has many advantages. Today, thousands of obsolete compact fluorescent lamps are used in underground coal mines. Replacing these with LED lamps requires RAMP documentation for each ST& E. This could require MSHA to review virtually thousands of RAMPS that would require an exorbitant amount of resources and would create backlogs that would take MSHA potentially years to process. This problem is addressed by the luminaire comparison method (LCM) that determines if the luminous intensity from a luminaire with a replacement lamp meets or exceeds the luminous intensity of the luminaire with a legacy lamp. If it does, then the replacement lamp is determined as equivalent and can be used as a replacement. One RAMP can then replace multiple RAMPs that would be required without the LCM. One company estimates over 700,000 USD in savings; another company has saved about 200,000 USD using the LCM. It is projected that the LCM will save millions of dollars. This article provides several examples to illustrate the LCM.

Protecting miners from coal bursts during development above historic mine workings in Harlan County, KY

In order to reach a large, untapped reserve of high-quality coal, D8 Cloverlick Mine proposed to mine a corridor nearly 600 m deep beneath the Benham Spur of Black Mountain, Kentucky’s highest peak. D8 Cloverlick Mine was extracting the Owl seam, but the corridor’s route lay approximately 20 m above century-old mine workings in the C–(Darby) seam. Adding to the concern, three serious coal bursts had recently occurred in nearby Owl seam workings. Maps of the old workings seemed to indicate that the underlying C–seam had been fully extracted. However, two of the coal bursts had occurred above areas where the C–Seam was also shown as mined out. Mine Safety and Health Administration (MSHA) Technical Support therefore investigated the records of past mining to better understand the old mine maps. Underground conditions observed in current Owl seam workings were also compared with the maps of the old C–seam workings. The study concluded that the presence of hazardous underlying remnants could not be ruled out. To mitigate the burst risk, D8 Cloverlick Mine adopted a strategy of stress probe drilling. A self-propelled coal drill was used to auger 11.5-m-long, small diameter holes in advance of mining. As each hole was drilled, the cuttings were measured to detect the presence of highly stressed coal. Ultimately the crossing was successfully completed without incident.

Identifying Risk Factors from MSHA Accidents and Injury Data Using Logistic Regression

The global mining industry has recorded significant declines in accident and injury rates attributed to the advancement in technology, increased enforcement, and safety consciousness. A goal of the mining industry is to achieve zero injury and occupational illness on all mine sites, prompting increased research into ways to further reduce mine accidents. A machine learning technique known as multiclass logistic regression is applied on a 10-year injury dataset from the Mine Safety and Health Administration (MSHA) to determine a miner’s susceptibility to a class of injury and to help identify significant risk factors associated with different classes of injury. The data is aggregated based on injury classification to provide statistically relevant results. The analysis identifies specific risk factors that influence a mine worker’s susceptibility to a given class of injury, i.e., non-fatal with no days lost or restricted activity, non-fatal with days lost and/or days of restricted work activity, and fatal and total permanent or partial permanent disability. These factors include miner’s age, mine type (coal vs. non-coal), experience on the current job (years), shift start time, employment type (operator vs. contractor), mining district, and type of accident. The results of the analysis indicate that a miner’s experience on the job, i.e., the number of years worked in a current job, is a significant risk to injury occurrence, even for those with decades of total mining experience. We further show the differences and similarities between the surface and underground mine incidents.

Simulation and Measurement of the Magnetic Field Coupling from a Proximity Detection System to Trailing Cables

Some of the common and serious hazards in underground mines are the dangers of being pinned, crushed, or struck by a large mining machine such as a scoop or a continuous mining machine (CMM). Proximity detection systems (PDSs) have been applied to protect miners from these hazards. The primary components of PDSs that are currently approved by the Mine Safety and Health Administration (MSHA) for use in underground coal mines are machine-mounted magnetic field generators and a miner-worn component (MWC), which measures the strength of the magnetic fields produced by the generators. Since these systems are magnetic field based, they can be adversely impacted by nearby cables due to parasitic coupling. Some mobile equipment in underground mines is electrically powered by long trailing cables that are pulled through the mine behind the equipment. Because the components of the PDS (generators and MWC) are frequently in close proximity to these cables, parasitic coupling can occur. Researchers from the National Institute for Occupational Safety and Health investigated the influence of trailing cables on the performance of PDSs. In particular, a three-phase model was proposed to describe the coupling process. The factors for controlling the magnetic field coupled from a field generator to a trailing cable were experimentally studied. The results show that the coupling is primarily controlled by two factors: the distance between the PDS components and the cable and the impedance between the cable and the ground. The coupling can be mitigated by either maintaining some minimum separation distance between the PDS components and the cable or increasing the impedance between the cable and the ground. The results presented in this article can help PDS manufacturers to design systems that are more immune to these effects.

Investigation of Machine-Mounted Area Lighting to Reduce Risk of Injury from Slips-Trips-Falls for Operators of Mobile Surface Mining Equipment.

Nonfatal injuries from slips, trips, and falls (STF) that occur at surface mines can result from inadequate lighting. Mobile equipment operators are among the occupations associated with the nonfatal incidents reported to the U.S. Mine Safety and Health Administration (MSHA). In addition, getting on/off the equipment (ingress/egress) frequently adds to the highest proportion of nonfatal incidents. Accordingly, researchers at the Pittsburgh Mining Research Division (PMRD), National Institute for Occupational Safety and Health (NIOSH) conducted a field study to investigate lighting on haul trucks and wheel loaders with regard to glare and illuminance levels recommended by the Illuminating Engineering Society (IES). The objective was to determine whether two light-emitting diode (LED) area luminaires-a Mr. Beams® (model MB390 Ultrabright) (area luminaire-1) and a NIOSH-developed Saturn (custom-designed for a mine roof bolter study) (area luminaire-2)-could complement a headlamp luminaire. Measured levels of visual tasks, with the headlamp alone and the area luminaires plus the headlamp, demonstrated that illuminance met or exceeded IES-recommended levels. Nevertheless, the area luminaires illuminated a much broader area, which is key to increasing hazard awareness. Discomfort and disability glare were lower with area luminaire-1 than with area luminaire-2. Differences in glare were more noticeable for newer models of haul trucks and loaders featuring updated ingress/egress system designs. This study demonstrates that commercially available luminaires, such as area luminaire-1, are capable of complementing headlamp lighting, and can thus improve a miner's ability to detect and avoid STF hazards.

Automated Baghouse Troubleshooting Diagnostics Maximize Efficiency: Saving Money, Improving Health, and Ensuring Compliance

Automated baghouse troubleshooting systems ensure optimized filtration efficiencies to minimize particulate emissions for a cleaner, safer, healthier, and compliant workplace. This article describes an additional benefit that is often overlooked. Automated technologies also reduce baghouse operating and maintenance costs. They convert perceived U.S. Environmental Protection Agency (EPA) and U.S. Mine Safety and Health Administration (MSHA) expense requirements into real and relatively quick returns on investments.

Occupational Respirable Mine Dust and Diesel Particulate Matter Hazard Assessment in an Underground Gold Mine in Ghana.

Background.Underground miners can experience occupational health diseases due to exposure to particulate matter hazards.Objectives.The aim of the present study was to examine occupational exposures of underground miners to dust and diesel particulate matter and to identify exposure groups with high potential to develop associated health effects due to the presence of dust and diesel particulate matter (DPM) hazards in an underground gold mine in Ghana.Methods.Purposive sampling was employed using gravimetric air samplers over an 8-hour time weighted average period. The National Institute for Occupational Safety and Health (NIOSH) analytical Chapter Q and 5040 were used in determining crystalline silica dust and diesel particulate matter fractions, respectively. Structured questionnaires were administered to gather data on workers' level of awareness to dust and DPM exposures.Results.It was found that 41% of the sampled groups were exposed to higher crystalline silica levels above the (NIOSH) permissible exposure limit (PEL) level of 0.05 mg/m3. For DPM, 49% of these groups had exposures above the Mine Safety and Health Administration (MSHA) PEL level of 160 μg/m3. Among the 94 mine workers who responded to this study, 62% were found to be aware of the presence and hazardous nature of silica dust, 28% had minimal knowledge and the remaining were found to be unaware.Conclusions.There are varying levels of dust and DPM due to the presence of silica-bearing rocks, the production of diesel fumes and inefficiencies of available mitigation measures. Research carried out over the past decades has found confirmed cases of silicosis and lung cancer due to high dust exposure levels. Rock drillers, blast men and shotcrete operators were found to be exposed to higher levels of dust and diesel particulate matter and are at greater risk of silicosis.Participant Consent.ObtainedEthics Approval.This study was approved by the Ethics Committee of the Kwame Nkrumah University of Science and Technology, Ghana and carried out under full consent of the mining company under study.Competing Interests.The authors declare no competing financial interests.