• Economic and Environmental Geology
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• 제55권3호
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• pp.241-259
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• 2022

In general, the REE were produced by mining conventional deposits, such as the carbonatite or the clay-hosted REE deposits. However, because of the recent demand increase for REE in modern industries, unconventional REE deposits emerged as a necessary research topic. Among the unconventional REE recovery methods, the REE-bearing coal deposits are recently receiving attentions. R-types generally have detrital originations from the bauxite deposits, and show LREE enriched REE patterns. Tuffaceous-types are formed by syngenetic volcanic activities and following input of volcanic ash into the basin. This type shows specific occurrence of the detrital volcanic ash-driven minerals and the authigenic phosphorous minerals focused at narrow horizon between coal seams and tonstein layers. REE patterns of tuffaceous-types show flat shape in general. Hydrothermal-types can be formed by epigenetic inflow of REE originated from granitic intrusions. Occurrence of the authigenic halogen-bearing phosphorous minerals and the water-bearing minerals are the specific characteristics of this type. They generally show HREE enriched REE patterns. Each type of REE-bearing coal deposits may occur by independent genesis, but most of REE-bearing coal deposits with high REE concentrations have multiple genesis. For the case of the US, the rare earth oxides (REO) with high purity has been produced from REE-bearing coals and their byproducts in pilot plants from 2018. Their goal is to supply about 7% of national REE demand. For the coal deposits in Korea, lignite layers found in Gyungju-Yeongil coal fields shows coexistence of tuff layers and coal seams. They are also based in Tertiary basins, and low affection from compaction and coalification might resulted into high-REE tuffaceous-type coal deposits. Thus, detailed geologic researches and explorations for domestic coal deposits are required.

• Journal of Intelligence and Information Systems
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• 제28권1호
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.