Respirable coal mine dust (RCMD) still poses serious occupational health hazards to coal miners and can lead to incurable lung diseases such as coal workers' pneumoconiosis (CWP, also referred to as "black lung"). Further, CWP can develop into a more severe form known as progressive massive fibrosis (PMF). There has been a resurgence of PMF since the late 1990s. Coal miners are also exposed to crystalline silica, which can lead to a lung disease known as silicosis. While coal mining related disease is on the rise, the historic dust monitoring data does not indicate such a striking resurgence. As a result, there has been an increased interest in research surrounding RCMD to understand exposure as well as prevent health effects.
Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX) is a powerful tool that can analyze RCMD on a particle-level. The images produced by the SEM can size and characterize morphology of micron and submicron-sized particles. In addition, the EDX can determine elemental content, which can be used to infer mineralogy. However, particle classification can be impacted by interferences due to particle loading density (PLD) and agglomeration. PLD refers to the number of particles per unit area of substrate, while agglomeration describes clustered or overlapping particles. This thesis includes two studies aimed at exploring the effects of both PLD and agglomeration on SEM-EDX analysis.
Study 1 includes an investigation into the effect of PLD on RCMD classification by SEM-EDX analysis. Dust recovered from the sample parent filters under low and high PLD conditions were used to isolate the effect of PLD. The comparison between the low and high PLD filters was then used to establish modified classification criteria to correct for high PLD. When the modified criteria were then applied to RCMD particles analyzed direct-on-filter, minimal change was observed in the apparent mineralogy distributions for most samples. These results suggest that particle agglomeration may have substantial effects on the particle classification of respirable dust analyzed direct-on-filter.
Study 2 includes an investigation into the effect of particle agglomeration on RCMD by SEM-EDX analysis. Automated and manual SEM-EDX analysis was performed on paired filters collected from a parent filter. The manual analysis targeted respirable silica containing agglomerates. Each pair consisted of a filter analyzed directly and a filter that underwent a recovery process to deposit dust particles onto a new filter. The mineralogy distributions from the automated analysis suggest that agglomeration affects sizing and particle classification. Based on the manual analysis, there was an apparent increase in independent silica and a decrease in respirable silica-containing agglomerates after the recovery process. A limited collection of passive samples revealed more agglomerates than on the filters that were collected using a pump and size-selector cyclone.
The work in this thesis is relevant to the research efforts aimed at the resurgence of coal mining related lung diseases, as the use of SEM-EDX can characterize RCMD by geographic region, geology, and location within a mine. Future work in this area of study might look at methods to estimate PLD in the field, other dust recovery methods, and a comparison of sampling methods. / Master of Science / Respirable coal mine dust (RCMD) still poses serious occupational health hazards to coal miners and can lead to incurable lung diseases such as coal workers' pneumoconiosis (CWP, also referred to as "black lung"). Further, CWP can develop into a more severe form known as progressive massive fibrosis (PMF). There has been a resurgence of PMF since the late 1990s. Coal miners are also exposed to crystalline silica, which can lead to a lung disease known as silicosis. While coal mining related disease is on the rise, the historic dust monitoring data does not indicate such a striking resurgence. As a result, there has been an increased interest in research surrounding RCMD to understand exposure as well as prevent health effects.
Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX) is a powerful tool that can analyze RCMD on a particle-level. SEM-EDX analysis can provide data on the size and elemental content of individual particles. The elemental content of each particle can be used to infer the mineralogy. However, the effectiveness of SEM-EDX analysis is dependent on sample conditions. Interference can occur if the samples are loaded with too many particles close together or clustered particles.
In Study 1, a modified classification criteria to correct for overloading was created by using a dataset that consisted of paired filters with ideal loading and overloading. The modified criteria were applied to a second dataset, resulting in minimal change on the filters analyzed directly indicating that clustered particles may be present. Study 2 utilized automated and manual SEM-EDX analysis on paired filters, one filter was analyzed directly, and the other filter was dispersed using a recovery method. The automated results suggest that clustered particles can affect the sizing and particle classification. The manual analysis, which looked at clusters containing silica, revealed that less clusters were present on the filters that underwent the recovery process. A collection of passive samples exhibited more clustered particles compared to direct filters collected using a pump and size-selector. These findings are relevant to the research efforts aimed at the resurgence of coal mining related lung diseases, as the use of SEM-EDX analysis can characterize RCMD by geographic region, geology, and location within a mine.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119246 |
| Date | 03 June 2024 |
| Creators | Sweeney, Daniel Joseph |
| Contributors | Mining Engineering, Sarver, Emily Allyn, Keles, Cigdem, Zhang, Wencai |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
Page generated in 0.0097 seconds