Iron nanoparticles prepared from South African acid mine drainage for the treatment of methylene blue in wastewater.

In this study, three acid mine drainage (AMD) sources were investigated as potential sources of iron for the synthesis of iron nanoparticles using green tea extract (an environmentally friendly reductant) or sodium borohydride (a chemical reductant). Electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), oxidation-reduction potential (ORP), ion chromatography (IC), and inductively coupled plasma-mass spectroscopy (ICP-MS) techniques were used to characterize the AMD, and the most suitable AMD sample was selected based on availability. Additionally, three tea extracts were characterized using ferric-reducing antioxidant power (FRAP) and 2,2-diphenyl-1-picryl-hydrazine-hydrate (DPPH), and the most suitable environmentally friendly reductant was selected based on the highest FRAP (1152 µmol FeII/g) and DPPH (71%) values. The synthesized iron nanoparticles were characterized and compared using XRD, STEM, Image J, EDS, and FTIR analytical techniques. The study shows that the novel iron nanoparticles produced using the selected green tea (57 nm) and AMD were stable under air due to the surface modification by polyphenols contained in green tea extract, whereas the nanoparticles produced using sodium borohydride (67 nm) were unstable under air and produced a toxic supernatant. Both the AMD-based iron nanoparticles can be used as Fenton-like catalysts for the decoloration of methylene blue solution. While 99% decoloration was achieved by the borohydride-synthesized nanoparticles, 81% decoloration was achieved using green tea-synthesized nanoparticles.

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.

Sulfur and oxygen isotope constraints on sulfate sources and neutral rock drainage-related processes at a South African colliery.

Understanding the fundamental controls that govern the generation of mine drainage is essential for waste management strategies. Combining the isotopic composition of water (H and O) and dissolved sulfate (S and O) with hydrogeochemical measurements of surface and groundwater, microbial analysis, composition of sediments and precipitates, and geochemical modeling results in this study we discussed the processes that control mine water chemistry and identified the potential source(s) and possible mechanisms governing sulfate formation and transformation around a South African colliery. Compared to various South African water standards, water samples collected from the surroundings of a coal waste disposal facility had elevated Fe2+ (0.9 to 56.9 mg L-1), Ca (33.0 to 527.0 mg L-1), Mg (6.2 to 457.0 mg L-1), Mn (0.1 to 8.6 mg L-1) and SO4 (19.7 to 3440.8 mg L-1) and circumneutral pH. The pH conditions are mainly controlled by the release of H+ from pyrite oxidation and the subsequent dissolution of carbonates and aluminosilicate minerals. The phases predicted to precipitate by equilibrium calculation were green rusts, ferrihydrite, gypsum, ±epsomite. Low concentrations of deleterious metals in solution are due to their low abundance in the local host rocks, and their attenuation through adsorption onto secondary Fe precipitates and co-precipitation at the elevated pH values. The δ34S values of sulfate are enriched (-6.5 ‰ to +5.6 ‰) compared to that of pyrite sampled from the mine (mean -22.5 ‰) and overlap with that of the organic sulfur of coal from the region (-2.5 to +4.9 ‰). The presence of both sulfur reducing and oxidizing bacteria were detected in the collected sediment samples. Combined, the data are consistent with the dissolved sulfate in the sampled waters from the colliery being derived primarily from pyrite probably with the subordinate contribution of organic sulfur, followed by its partial removal through precipitation and microbially-induced reduction.