Abstract
The health risks associated with exposure to respirable dust in coal mines have been attracted much attention by an increasing number of researchers. However, the accurate identification of potentially hazardous agents in respirable dusts and the evaluation of the potential health risks arising from it still remains controversial to varying degrees. A comprehensive understanding of the physicochemical properties of respirable dust is a prerequisite and an important basis for resolving this controversy. Therefore, in this study, the particle size distribution and morphology, pore structure, mineralogical and geochemical patterns, and oxidative potential (OP) of respirable coal mine dust were comprehensively investigated. Stepwise multiple linear regression was employed to identify dust components driving OP, such as anatase, tobelite, quartz, and ankerite, in respirable coal mine dust, along with Na, Ni, Se, W, and As. On this basis, we performed a single-factor risk prediction for different coal mines by considering factors that may impact miners' health, with the analyses yielding somewhat contradictory results. Therefore, a multifactor integrated prediction model is proposed using an entropy-based technique for order preference by similarity to the ideal solution to categorize coal mines in the study area into three risk categories, high-, medium-, and low-risk dust mines, which is important for the hierarchical classification and control of coal mines and for formulating appropriate dust prevention and control measures.
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