Computational risk modeling of underground coal mines based on NIOSH employment demographics.

Objective: To investigate the feasibility of predicting the risk of underground coal mine operations using data from the National Institute for Occupational Safety and Health (NIOSH). Methods: A total of 22,068 data entries from 3,982 unique underground coal mines from 1990 to 2020 were extracted from the NIOSH mine employment database. We defined the risk index of a mine as the ratio between the number of injuries and the size of the mine. Several machine learning models were used to predict the risk of a mine based on its employment demographics (i.e., number of underground employees, number of surface employees, and coal production). Based on these models, a mine was classified into a "low-risk" or "high-risk" category and assigned with a fuzzy risk index. Risk probabilities were then computed to generate risk profiles and identify mines with potential hazards. Results: NIOSH mine demographic features yielded a prediction performance with an AUC of 0.724 (95% CI 0.717-0.731) based on the last 31-years' mine data and an AUC of 0.738 (95% CI: 0.726, 0.749) on the last 16-years' mine data. Fuzzy risk score shows that risk is greatest in mines with an average of 621 underground employees and a production of 4,210,150 tons. The ratio of tons/employee maximizes the risk at 16,342.18 tons/employee. Conclusion: It is possible to predict the risk of underground coal mines based on their employee demographics and optimizing the allocation and distribution of employees in coal mines can help minimize the risk of accidents and injuries.

Investigation of respirable coal mine dust (RCMD) and respirable crystalline silica (RCS) in the U.S. underground and surface coal mines.

Dust is an inherent byproduct of mining activities that raises notable health and safety concerns. Cumulative inhalation of respirable coal mine dust (RCMD) and respirable crystalline silica (RCS) can lead to obstructive lung diseases. Despite considerable efforts to reduce dust exposure by decreasing the permissible exposure limits (PEL) and improving the monitoring techniques, the rate of mine workers with respiratory diseases is still high. The root causes of the high prevalence of respiratory diseases remain unknown. This study aimed to investigate contributing factors in RCMD and RCS dust concentrations in both surface and underground mines. To this end, a data management approach is performed on MSHA's database between 1989 and 2018 using SQL data management. In this process, all data were grouped by mine ID, and then, categories of interests were defined to conduct statistical analysis using the generalized estimating equation (GEE) model. The total number of 12,537 and 9050 observations for respirable dust concentration are included, respectively, in the U.S. underground and surface mines. Several variables were defined in four categories of interest including mine type, geographic location, mine size, and coal seam height. Hypotheses were developed for each category based on the research model and were tested using multiple linear regression analysis. The results of the analysis indicate higher RCMD concentration in underground compared to RCS concentration which is found to be relatively higher in surface coal mines. In addition, RCMD concentration is seen to be higher in the Interior region while RCS is higher in the Appalachia region. Moreover, mines of small sizes show lower RCMD and higher RCS concentrations. Finally, thin-seam coal has greater RCMD and RCS concentrations compared to thicker seams in both underground and surface mines. In the end, it is demonstrated that RCMD and RCS concentrations in both surface and underground mines have decreased. Therefore, further research is needed to investigate the efficacy of the current mass-concentration-based monitoring system.

Challenges in Selecting an Appropriate Heat Stress Index to Protect Workers in Hot and Humid Underground Mines.

BACKGROUND: A detailed evaluation of the underground mine climate requires extensive measurements to be performed coupled to climatic modeling work. This can be labor-intensive and time-consuming, and consequently impractical for daily work comfort assessments. Therefore, a simple indicator like a heat stress index is needed to enable a quick, valid, and acceptable evaluation of underground climatic conditions on a regular basis. This can be explained by the unending quest to develop a "universal index," which has led to the proliferation of many proposed heat stress indices. METHODS: The aim of this research study is to discuss the challenges in identifying and selecting an appropriate heat stress index for thermal planning and management purposes in underground mines. A method is proposed coupled to a defined strategy for selecting and recommending heat stress indices to be used in underground metal mines in the United States and worldwide based on a thermal comfort model. RESULTS: The performance of current heat stress indices used in underground mines varies based on the climatic conditions and the level of activities. Therefore, carefully selecting or establishing an appropriate heat stress index is of paramount importance to ensure the safety, health, and increasing productivity of the underground workers. CONCLUSION: This method presents an important tool to assess and select the most appropriate index for certain climatic conditions to protect the underground workers from heat-related illnesses. Although complex, the method presents results that are easy to interpret and understand than any of the currently available evaluation methods.

A Method to Protect Mine Workers in Hot and Humid Environments.

BACKGROUND: Work comfort studies have been extensively conducted, especially in the underground and meteorological fields resulting in an avalanche of recommendations for their evaluation. Nevertheless, no known or universally accepted model for comprehensively assessing the thermal work condition of the underground mine environment is currently available. Current literature presents several methods and techniques, but none of these can expansively assess the underground mine environment since most methods consider only one or a few defined factors and neglect others. Some are specifically formulated for the built and meteorological climates, thus making them unsuitable to accurately assess the climatic conditions in underground development and production workings. METHODS: This paper presents a series of sensitivity analyses to assess the impact of environmental parameters and metabolic rate on the thermal comfort for underground mining applications. An approach was developed in the form of a "comfort model" which applied comfort parameters to extensively assess the climatic conditions in the deep, hot, and humid underground mines. RESULTS: Simulation analysis predicted comfort limits in the form of required sweat rate and maximum skin wettedness. Tolerable worker exposure times to minimize thermal strain due to dehydration are predicted. CONCLUSION: The analysis determined the optimal air velocity for thermal comfort to be 1.5 m/s. The results also identified humidity to contribute more to deviations from thermal comfort than other comfort parameters. It is expected that this new approach will significantly help in managing heat stress issues in underground mines and thus improve productivity, safety, and health.