Reflectance Spectral Features and Significant Minerals in Kaishantun Ophiolite Suite, Jilin Province, NE China

Relationship of Mineralogical Composition to Thermal Expansion, Spectral Reflectance, and Physico-Mechanical Aspects of Commercial Ornamental Granitic Rocks

The aim of the present study is to link the thermal expansion, spectral reflectance, and physico-mechanical aspects of different types of commercial granitic rocks with their mineralogical and chemical composition. The granitic rock types were characterized using several analyses, including petrography, chemical, mineralogical, and thermo-gravimetrical analysis using XRF, XRD, and TG/DTG/DSC techniques. The rock types were subjected to several performance tests, such as tests of their thermal expansion, spectral reflectance, and physico-mechanical properties. The results revealed that quartz, albite, and potash feldspar with minor amounts of mica (biotite/muscovite/annite) are the main mineralogical constitutes, in addition to some alteration minerals, such as kaolinite, saussorite, and prehnite. The studied granitic rocks were classified as monzogranite/syenogranite of high K-calc-alkaline and peraluminous characters and are related to late- to post-collisional settings. The thermogravimetrical analysis revealed that the overall mass loss over the whole temperature range up to 978 °C did not exceed 3% of the initial weight for all studied rocks. The results of thermal expansion revealed that the maximum change in linear thermal expansion for all rock types did not exceed 0.015% of their initial lengths at an unusual air temperature of 50 °C. The spectral analysis revealed that iron and hydroxyl ions are the main spectral absorption features that appeared in the VIS-NIR and SWIR regions, in addition to the appearance of the common and distinctive absorption peaks of the main mineral composition. Furthermore, the spectral reflectance demonstrated that the granitic rock types of low iron oxide content achieved a high reflectivity percent in the VIS-NIR and SWIR spectral regions compared with those of high iron content. As a general trend, the granitic rock types of high iron content and/or lower quartz content exhibited a high performance regarding their physical and mechanical properties, such as water absorption, apparent porosity, bulk density, compressive strength, and abrasion resistance. The results of water absorption, density, strength, and abrasion resistance of the studied granitic rocks are in the range of 0.14–0.31%, 2582–2644 kg/m³, 77.85–222.75 MPa, and 26.27–55.91 Ha, respectively, conforming to the requirements of ornamental stones according to the ASTM standard.

Mining Rock Wastes for Water Treatment: Potential Reuse of Fe- and Mn-Rich Materials for Arsenic Removal

The worldwide mining industry produces millions of tons of rock wastes, raising a considerable burden for managing both economic and environmental issues. The possible reuse of Fe/Mn-rich materials for arsenic removal in water filtration units, along with rock properties, was evaluated. By characterizing and testing 47 samples collected from the Joda West Iron and Manganese Mine in India, we found As removal up to 50.1% at 1 mg/L initial As concentration, with a corresponding adsorption capacity of 0.01–0.46 mgAs/g mining waste. The As removal potential was strictly related to spectral, mineralogical, and elemental composition of rock wastes. Unlike rock crystallinity due to quartz and muscovite, the presence of hematite, goethite, and kaolinite, in association with the amorphous fractions of Fe and Al, enhanced the As adsorption. The natural content of arsenic indicated itself the presence of active sorptive sites. The co-occurrence of site-specific competitors (i.e., phosphate) represented a consequent limitation, whereas the content of Ce, Cu, La, and Pb contributed positively to the As adsorption. Finally, we proposed a simplified multiple linear model as predictive tool to select promising rock wastes suitable for As removal by water filtration in similar mining environments: As predicted = 0.241 + 0.00929[As] + 0.000424[La] + 0.000139[Pb] − 0.00022[P].