Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate in the lungs of mineworkers. The lung deposited surface area (LDSA) concentration is a variant solution to evaluate potential health impacts. The aim of this study is to analyse PM and LDSA concentrations in the operational workings of the oil shale underground mine. Experimental measurements were carried out by a direct-reading real-time PM monitor, Dusttrak DRX, and a multimetric fine particle detector, Naneous Partector 2, during the loading and dumping processes using the diesel engine loader. Consequently, the analysis was conducted on PM, LDSA, particle surface area concentration (SA), average particle diameter (d), particle number concentration (PNC), and particle mass (PM_0.3), producing a few valuable correlation factors. Averaged LDSA was around 1433 μm²/cm³ and reached maximum peaks of 2140 μm²/cm³ during the loading, which was mostly related to diesel exhaust emissions, and within the dumping 730 μm²/cm³ and 1840 μm²/cm³, respectively. At the same time, average PM₁ was about 300 μg/ m³ during the loading, but within the dumping peaks, it reached up to 10,900 μg/ m³. During the loading phase, particle diameter ranged from 30 to 90 nm, while during the dumping phase peaks, it varied from 90 to 160 nm. On this basis, a relationship between PNC and particle diameter has been produced to demonstrate an approximate split between diesel particulate matter (DPM) and oil shale dust diameters. This study offers important data on PM and LDSA concentration that can be used for estimating potential exposure to miners at various working operations in the oil shale underground mines, and will be used for air quality control in accordance with establishing toxic aerosol health effects.

Most oil shale deposits in Kazakhstan were estimated without detailed calculations of the grade and tonnages and included low confidence categories, i.e., Inferred, Off-Balance, and Non-Economic oil shales, on which cannot be given any oil reserves. An analysis of Kazakhstan oil shale deposits in accordance with resource estimation practices for consideration of potential shale oil tonnages has been produced. The developed methodology considers extraction and processing recoveries of conventional and unconventional mining methods. The methodology uses Monte Carlo modeling to estimate a range of oil content and oil recoveries and uses the event tree analysis to demonstrate how the initial oil shale material tonnages and grades go through various fault and success branches, considering probabilities distributions and estimating potential shale oil tones at the end. As a result, this estimation methodology has been validated by high-ranked resource category oil shale deposits, which demonstrated the range of potential shale oil in the range of 10.7–16.8 Mt at the 50% confidence level. The results will be used for further consideration in financial-economic feasibility studies, which must take into account operational and capital expenses, product sale prices and market, and social-environmental aspects.