Geochemistry and oxidative potential of the respirable fraction of powdered mined Chinese coals

Influence of magma intrusion on coal geochemical characteristics: a case study of Tiefa Daxing coal mine

Magma intrusion has an important influence on the physical and mechanical properties of coal and rock. In the area of magma intrusion, disasters such as gas outburst are prone to occur. Revealing its invasion law will be conducive to disaster management and energy development. For this purpose, changes in industrial analysis components of coal, mineral composition, major oxides, trace elements, and rare earth elements of coal under the thermal metamorphism of magma intrusion were analyzed. It is found that the moisture and volatile matter contents of the thermally affected coals in the mining face are generally lower than that of normal coals, while moisture and volatile matter contents are reduced towards to the magma intrusion contact. For example, the moisture and volatile matter of coal sample M01 decreased by 64.6% and 38.6% respectively compared with coal sample M05. During magma intrusion, some minerals remain on the surface of the coal body, resulting in changes in the mineral composition of the coal body. The decrease in carbon atom net spacing, the increase in crystallite aggregation and ductility, and aromaticity in thermally affected coals have a positive impact on the improvement of coal metamorphism. Due to the influences of magmatic intrusion, the variation rules of major oxides in coal are different, and the closer to the magmatic intrusion zone, the easier the major oxides are to be depleted. However, magma intrusion will not lead to the loss of all major oxides in thermally affected coals, such as content of CaO is 54.8%, which is higher than that of coal not affected by magmatic hydrothermal fluid. Most of the trace elements in the thermally affected coals of the No. 9 coal seam are depleted. The contents of rare earth elements are low on the whole coalbasis, with an average of 29.48 μg/g, and the distribution pattern towards to magmatic intrusion shows a wide and gentle "V" curve with left high and right low, showing the characteristics of enrichment of light rare earth elements.

Machine Learning Demonstrates Dominance of Physical Characteristics over Particle Composition in Coal Dust Toxicity

Mine dust has been linked to the development of pneumoconiotic diseases such as silicosis and coal workers' pneumoconiosis. Currently, it is understood that the physicochemical and mineralogical characteristics drive the toxic nature of dust particles; however, it remains unclear which parameter(s) account for the differential toxicity of coal dust. This study aims to address this issue by demonstrating the use of the partial least squares regression (PLSR) machine learning approach to compare the influence of D50 sub 10 μm coal particle characteristics against markers of cellular damage. The resulting analysis of 72 particle characteristics against cytotoxicity and lipid peroxidation reflects the power of PLSR as a tool to elucidate complex particle-cell relationships. By comparing the relative influence of each characteristic within the model, the results reflect that physical characteristics such as shape and particle roughness may have a greater impact on cytotoxicity and lipid peroxidation than composition-based parameters. These results present the first multivariate assessment of a broad-spectrum data set of coal dust characteristics using latent structures to assess the relative influence of particle characteristics on cellular damage.

The impact of coal mine dust characteristics on pathways to respiratory harm: investigating the pneumoconiotic potency of coals

Exposure to dust from the mining environment has historically resulted in epidemic levels of mortality and morbidity from pneumoconiotic diseases such as silicosis, coal workers' pneumoconiosis (CWP), and asbestosis. Studies have shown that CWP remains a critical issue at collieries across the globe, with some countries facing resurgent patterns of the disease and additional pathologies from long-term exposure. Compliance measures to reduce dust exposure rely primarily on the assumption that all "fine" particles are equally toxic irrespective of source or chemical composition. For several ore types, but more specifically coal, such an assumption is not practical due to the complex and highly variable nature of the material. Additionally, several studies have identified possible mechanisms of pathogenesis from the minerals and deleterious metals in coal. The purpose of this review was to provide a reassessment of the perspectives and strategies used to evaluate the pneumoconiotic potency of coal mine dust. Emphasis is on the physicochemical characteristics of coal mine dust such as mineralogy/mineral chemistry, particle shape, size, specific surface area, and free surface area-all of which have been highlighted as contributing factors to the expression of pro-inflammatory responses in the lung. The review also highlights the potential opportunity for more holistic risk characterisation strategies for coal mine dust, which consider the mineralogical and physicochemical aspects of the dust as variables relevant to the current proposed mechanisms for CWP pathogenesis.

Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain

Ambient particulate matter (PM) is a major contributor to air pollution, leading to adverse health effects on the human population. It has been suggested that the oxidative potential (OP, as a tracer of oxidative stress) of PM is a possible determinant of its health impact. In this study, samples of PM₁₀, PM_2.5, and PM₁ were collected roughly every four days from January 2018 until March 2019 at a Barcelona urban background site and Montseny rural background site in northeastern Spain. We determined the chemical composition of samples, allowing us to perform source apportionment using positive matrix factorization. The OP of PM was determined by measuring reactive oxygen species using dithiothreitol and ascorbic acid assays. Finally, to link the sources with the measured OP, both a Pearson's correlation and a multiple linear regression model were applied to the dataset. The results showed that in Barcelona, the OP of PM₁₀ was much higher than those of PM_2.5 and PM₁, whereas in Montseny results for all PM sizes were in the same range, but significantly lower than in Barcelona. In Barcelona, several anthropogenic sources were the main drivers of OP in PM₁₀ (Combustion + Road Dust + Heavy Oil + OC-rich) and PM_2.5 (Road Dust + Combustion). In contrast, PM₁ -associated OP was driven by Industry, with a much lower contribution to PM₁₀ and PM_2.5 mass. Meanwhile, Montseny exhibited no clear drivers for OP evolution, likely explaining the lack of a significant difference in OP between PM₁₀, PM_2.5, and PM₁. Overall, this study indicates that size fraction matters for OP, as a function of the environment typology. In an urban context, OP is driven by the PM₁₀ and PM₁ size fractions, whereas only the PM₁ fraction is involved in rural environments.