Aerosol radiative forcing of forest fires unprecedented in South Korea (2022) captured by Korean geostationary satellites, GK-2A AMI and GK-2B GEMS.

The worst forest fires in Korean history broke out on March 4, 2022 and lasted for ten days. In order to monitor the catastrophic forest fires, Geostationary Korea Multi-Purpose Satellite (GK)-2 A Advanced Meteorological Imager (AMI) and GK-2B Geostationary Environment Monitoring Spectrometer (GEMS) data were used in this study. Aerosol optical depth (AOD) irretrievable for the biomass-burning aerosols produced with water vapor classified as could-contaminated, was reconstructed by ultraviolet aerosol index (UVAI). Afterward, aerosol radiative forcing (ARF) at TOA was finally estimated by the correlation of AOD and surface albedo with ARF. Most of the aerosols drifted toward the East Sea by the prevailing westerly winds, and caused a cooling effect on the atmosphere with a maximum daily average radiative forcing of -69.28 Wm-2. Furthermore, the fire-prone conditions for the unprecedented forest fires were discussed in detail as following aspects; 1) the most severe drought caused by a "triple-dip" La Niña; 2) pressure patterns and topographical features that generate strong winds; 3) coniferous forests prone to fires; and 4) increased human activity following the nationwide COVID-19 vaccination. This study demonstrated that the rapid and effective ARF estimation based on the satellite remote sensing can contribute to a better understanding of ARF in the Earth's radiation budget for the global forest fires that will be more frequent, intense, and longer-lasting due to the human-caused climate and environment changes.

Improved ANN-Based Approach Using Relative Impact for the Prediction of Thermal Coal Elemental Composition Using Proximate Analysis.

The basic properties of coal influence various procedures of power generation, such as the design of power generation plants, estimation of the current condition of boilers, and total efficiency of power plants. The elemental composition is a needed factor in evaluating the process of chemical conversion and predicting the flow of flue gas and the quality of air in coal combustion. In the past, several relationships have been established using ultimate and proximate analyses. This study aims to predict the elemental compositions of 104 thermal coals used for coal-fired power plants in South Korea using an artificial neural network (ANN) that uses proximate analysis values as input parameters. The ANN-based model was optimized with six activation functions and four hidden layers after evaluating various performance indices, including R 2, mean square error (MSE), and epoch, then additional calculations were derived to compare performances from previous research using the mean absolute error (MAE), average absolute error, and average bias error. It was found that the best topology was established using the Levenberg-Marquardt activation function and 10 hidden layers, resulting in the highest R 2 value and smallest MSE of all topologies tested. As a result, the relative impact on calculation accuracy was derived from ANN hidden layers to analyze prediction accuracies of carbon, hydrogen, and oxygen compositions. Accuracy was improved over previous results by 4.71-0.91% via coal rank division topology optimization. Based on the MAE, the current results are even close in performance to those of adaptive neuro-fuzzy inference systems. They also outperformed previous research models by 5.40 and 7.39% in terms of MAE accuracy. Applicability of the ANN was also analyzed with limitations of the chemical composition of ANNs and present reinforcement measures in the future studies through qualitative analysis comparisons based on Fourier transform infrared spectroscopy. Consequently, the relative effect was derived from the ANN hidden layer weight for specific calculation of the relationship between elemental composition and proximate analysis properties. As a result, it was possible to qualitatively analyze how the proximate analysis value affects the composition of elements and calculate the ratio accordingly. The findings of this study provide an improved and efficient approach to predicting the elemental composition of thermal coal, based on data from South Korean power plants. Also, further research can follow schematics from this study with the applicability and accessibility of the ANN.

Low-Temperature Oxidation Reactivity of Low-Rank Coals and Their Petrographic Properties.

Through the oxidation of coal at low temperatures and the resulting petrographic analysis, this study aims to predict spontaneous combustion, which has emerged as an industrial problem. Low-temperature oxidation analysis and the corresponding petrographic characteristics of four different coals treated under low temperatures of 25, 50, and 75 °C, which was set as the reactor temperature, were investigated. Low-temperature oxidation experiments designed at Pusan National University, based on papers related to low-temperature experiments, were conducted to analyze the constant of oxidation reactions. The petrographic characteristics of the coals were analyzed using a coal petrographic microscope spectrophotometer for determining their vitrinite reflectance and morphology, and the coals were extracted after the low-temperature oxidation experiments. After these analyses, vitrinite reflectance changed, and the normalized k, which is the difference between the constant of reaction from 25 °C to (the setting temperatures of) 50 and 75 °C, was also calculated. By comparing the oxidation rates of the coals and the corresponding petrographic analyses, the cause of spontaneous combustion can be deduced and a prediction can be made about which coal burns most efficiently at a low temperature.

Emission factors of air toxics from semiconductor manufacturing in Korea.

The development of local, accurate emission factors is very important for the estimation of reliable national emissions and air quality management. For that, this study is performed for pollutants released to the atmosphere with source-specific emission tests from the semiconductor manufacturing industry. The semiconductor manufacturing industry is one of the major sources of air toxics or hazardous air pollutants (HAPs); thus, understanding the emission characteristics of the emission source is a very important factor in the development of a control strategy. However, in Korea, there is a general lack of information available on air emissions from the semiconductor industry. The major emission sources of air toxics examined from the semiconductor manufacturing industry were wet chemical stations, coating applications, gaseous operations, photolithography, and miscellaneous devices in the wafer fabrication and semiconductor packaging processes. In this study, analyses of emission characteristics, and the estimations of emission data and factors for air toxics, such as acids, bases, heavy metals, and volatile organic compounds from the semiconductor manufacturing process have been performed. The concentration of hydrogen chloride from the packaging process was the highest among all of the processes. In addition, the emission factor of total volatile organic compounds (TVOCs) for the packaging process was higher than that of the wafer fabrication process. Emission factors estimated in this study were compared with those of Taiwan for evaluation, and they were found to be of similar level in the case of TVOCs and fluorine compounds.